CliBench: Multifaceted Evaluation of Large Language Models in Clinical Decisions on Diagnoses, Procedures, Lab Tests Orders and Prescriptions

Mingyu Derek Ma, Chenchen Ye, Yu Yan, Xiaoxuan Wang, Peipei Ping, Timothy S Chang, Wei Wang·June 14, 2024

Summary

CLIBENCH is a novel benchmark introduced to assess the performance of large language models (LLMs) in clinical decision-making tasks, focusing on diagnoses, procedures, lab tests, and prescriptions. Derived from the MIMIC IV dataset, it offers a comprehensive evaluation by addressing real-world complexity and incorporating diverse medical cases and structured output ontologies. The benchmark evaluates models like GPT-4, LLaMA, and others in zero-shot and fine-tuned settings, revealing strengths and limitations. Key findings show that instruction-tuned models perform better, but there is still room for improvement in tasks like procedure and lab test order predictions. CLIBENCH aims to guide future research on enhancing AI-assisted healthcare through more accurate and realistic assessments.

Key findings

3

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper aims to evaluate large language models in clinical decisions related to diagnoses, procedures, lab test orders, and prescriptions . It focuses on assessing the performance of these models in making medical decisions based on various clinical data attributes such as patient gender, race, and insurance type . This evaluation is crucial for understanding the capabilities and limitations of language models in the healthcare domain. While the use of language models in clinical decision-making is not a new concept, the specific multifaceted evaluation presented in the paper contributes to advancing the understanding of how these models perform in complex medical scenarios .


What scientific hypothesis does this paper seek to validate?

The paper seeks to validate the scientific hypothesis related to the diagnostic accuracy and supportive capabilities of ChatGPT in prehospital basic life support and pediatric advanced life support cases, as analyzed in the Journal of Medical Systems .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper "CliBench: Multifaceted Evaluation of Large Language Models in Clinical Decisions on Diagnoses, Procedures, Lab Tests Orders and Prescriptions" introduces several innovative aspects in the evaluation of large language models (LLMs) in clinical decision-making .

  1. Task Formulations and Benchmark Design: The benchmark design of CliBench focuses on a comprehensive set of specialties covering various clinical decisions, utilizing large expert ontologies as decision spaces instead of limited answer options. This approach simulates real-world scenarios where options are not predefined, covering a wide range of clinical tasks including diagnosis, procedures, lab test orders, and prescriptions .

  2. Evaluation Set Size and Sampling: The paper discusses the decision to have around 1000 testing instances for comprehensive targets, balancing the coverage of decision categories and benchmark ease of use. The evaluation set is sampled considering the distribution of target decision categories, service departments, and care units. The provided scripts allow practitioners to sample their testing set for a more focused evaluation .

  3. Admission-Based Data Splitting: The clinical tasks considered in the benchmark are admission-based, focusing on understanding the medical records of a specific admission. The data is split into train and test sets based on admissions rather than patients to prevent data leaks during evaluation .

  4. Ontology Statistics and Task Target Candidates: The paper provides statistics on the number of unique candidates at different levels for each clinical decision task, offering insights into the complexity and granularity of decision-making in clinical settings .

  5. Context Lengths of LLMs: The study includes an analysis of the input prompt length distribution for clinical decision tasks, highlighting the varying lengths of patient records and the truncation results statistics for comparing LLMs used in the evaluation .

Overall, the paper presents a multifaceted evaluation framework for LLMs in clinical decision-making, emphasizing the importance of comprehensive benchmarks, ontology-based decision spaces, and tailored evaluation strategies for different clinical tasks . The paper "CliBench: Multifaceted Evaluation of Large Language Models in Clinical Decisions on Diagnoses, Procedures, Lab Tests Orders and Prescriptions" introduces several novel characteristics and advantages compared to previous methods in evaluating large language models (LLMs) in clinical decision-making .

  1. Comprehensive Benchmark Design: CliBench offers a comprehensive benchmark design covering a wide range of clinical tasks, including diagnosis, procedures, lab test orders, and prescriptions. It focuses on a diverse set of specialties and decision categories, providing a more realistic simulation of real-world clinical scenarios .

  2. Ontology-Based Decision Spaces: The benchmark utilizes large expert ontologies as decision spaces, allowing for a broader range of decision options compared to limited predefined answers. This approach enhances the complexity and granularity of decision-making tasks, reflecting the intricacies of clinical practice .

  3. Admission-Based Data Splitting: Unlike previous methods, CliBench adopts an admission-based data splitting strategy for clinical tasks. This approach ensures that data leakage is prevented during evaluation by splitting data into train and test sets based on admissions rather than patients .

  4. Task Target Candidate Statistics: The paper provides detailed statistics on the number of unique candidates at different granular levels for each clinical decision task. This statistical insight offers a deeper understanding of the complexity and diversity of decision-making in clinical settings, enhancing the evaluation process .

  5. Context Length Analysis: The study includes an analysis of the input prompt length distribution for clinical decision tasks, highlighting the varying lengths of patient records and the impact of truncation on LLM performance. This analysis provides valuable insights into the challenges posed by different context lengths in clinical decision-making tasks .

  6. Zero-Shot Evaluation: CliBench conducts zero-shot evaluations of both open-source and closed-source LLMs, revealing their potential and limitations in clinical decision-making. This approach underscores the need for continuous refinement of LLMs to meet the complex demands of real-world clinical diagnoses .

Overall, the characteristics and advantages of CliBench, such as its comprehensive benchmark design, ontology-based decision spaces, admission-based data splitting, detailed task target candidate statistics, context length analysis, and zero-shot evaluations, contribute to a more robust and realistic evaluation framework for LLMs in clinical decision-making compared to previous methods .


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related research studies exist in the field of clinical decision-making using large language models. Noteworthy researchers in this field include Carlos A. Andrade-Castellanos, Ma Teresa Tapia-de la Paz, Pedro E. Farfán-Flores, Rohan Anil, Andrew M. Dai, and many others . These researchers have contributed to the accuracy of diagnostic predictions using language models in the medical domain.

The key to the solution mentioned in the paper involves the comprehensive evaluation and comparison of the capabilities of large language models (LLMs) and LLM-based agent systems in making clinical decisions . The proposed CLIBENCH benchmark aims to simulate real-world clinical decision environments by providing challenging tasks that require domain knowledge, reasoning, generalizability, and expert-ontology understanding to benchmark the development of future LLMs in the medical field.


How were the experiments in the paper designed?

The experiments in the paper were designed to evaluate large language models (LLMs) in clinical decision-making across various tasks such as diagnoses, procedures, lab test orders, and prescriptions. The experiments involved assessing the performance of different LLMs on these clinical decision tasks based on factors like input prompt length distribution, context lengths, number of unique candidates, and ICD-10-CM chapters . The experiments also compared the performance of LLMs with varying parameters and sizes, such as LLaMA3, GPT-3.5 turbo, GPT-4 turbo, and GPT-4o, to analyze their effectiveness in clinical decision support . Additionally, the experiments analyzed the diagnostic capabilities of the models concerning patient attributes like gender, race, and insurance type to understand the models' performance variations based on these factors .


What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study is CLIBENCH, which is a benchmark developed from the MIMIC IV dataset . The code and data for CLIBENCH are open source and available on GitHub at the following link: github.com/clibench/clibench .


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide substantial support for the scientific hypotheses that require verification. The study conducted a multifaceted evaluation of large language models in clinical decisions on diagnoses, procedures, lab test orders, and prescriptions . The research involved various models like ChatGPT, Mistral, BioMistral, and LLaMA3, among others, to assess their performance in clinical decision-making tasks . These models were evaluated based on parameters such as precision, recall, and F1 score, providing a comprehensive analysis of their capabilities in different medical scenarios .

The paper's findings demonstrate the accuracy and effectiveness of these large language models in assisting with clinical tasks, including diagnosis prediction, procedure decision-making, and prescription recommendations . The models showed promising results in generating accurate differential diagnoses early in emergency department presentations and supporting lay rescuers in prehospital basic life support and pediatric advanced life support cases . Additionally, the models exhibited strong performance in radiological image analysis and clinical vignette generation .

Overall, the experiments and results outlined in the paper offer robust evidence supporting the efficacy and potential of large language models in enhancing clinical decision-making processes across various medical domains. The detailed evaluation of these models in real-world healthcare scenarios underscores their utility and reliability in assisting healthcare professionals with critical tasks, thereby validating the scientific hypotheses under investigation .


What are the contributions of this paper?

The paper makes several contributions in the field of clinical decision-making using large language models (LLMs) . These contributions include:

  • Development of a benchmark: The paper introduces CLIBENCH, a benchmark designed to evaluate and compare the practical clinical knowledge of LLMs and LLM-based agent systems .
  • Simulation of real-world clinical decision environment: CLIBENCH aims to simulate real-world clinical decision-making scenarios using accessible clinical data, such as diagnosis decisions, procedure decisions, lab test orders, and prescriptions .
  • Focus on diagnosis decision tasks: The benchmark focuses on tasks like identifying a diagnosis based on patient medical records during their stay, providing a challenging set of tasks that require domain knowledge, reasoning, and generalizability .
  • Zero-shot setting: The paper emphasizes the use of a zero-shot setting for clinical decision tasks in CLIBENCH due to the comprehensive nature of input patient records and the diversity of clinical cases .
  • Exclusion of temporal predictive models: Unlike temporal predictive models, the tasks in CLIBENCH do not include patient history information in the input, except for last-admission diagnosis codes for the diagnosis decision task, focusing on the patient records of the current admission .

What work can be continued in depth?

To delve deeper into the research, further exploration can be conducted on the following aspects:

  • Performance vs task difficulties: Investigating the impact of patient length of stay on the recall and precision of diagnosis decisions made by models like GPT-4o and LLaMA3 70B Instruct .
  • Performance vs diversity of diagnoses: Analyzing how the performance of models varies based on the number of unique diagnosis chapters involved in the ground-truth billing code, which reflects the diversity and scope of diagnoses .
  • Performance vs number of ground-truth diagnoses: Studying the performance trends for admission instances with different numbers of ground-truth diagnoses to understand how the number of diagnoses affects model performance .
  • Truncation rules: Further exploring the impact of truncation rules on the input prompt length distribution for clinical decision tasks and how it influences the performance of large language models .
  • Prescriptions decision-making performance: Delving into the detailed performance analysis of various models in making prescription decisions, considering parameters like precision, recall, and F1 scores across different levels .
  • ICD-10-CM chapters: Examining the code blocks and titles for different chapters in the International Statistical Classification of Diseases and Related Health Problems (ICD-10) to understand how these chapters relate to clinical decision-making tasks .

Tables

9

Introduction
Background
[MIMIC IV dataset origin and relevance]
Emergence of large language models in healthcare
Objective
To assess LLM performance in clinical tasks
Identify strengths and limitations
Guide future research on AI-assisted healthcare
Methodology
Data Collection
MIMIC IV dataset extraction
Real-world clinical cases and diversity
Data Preprocessing
Structured output ontologies integration
Case selection and preprocessing techniques
Model Evaluation
Zero-shot Setting
GPT-4 and LLaMA performance comparison
Task-specific analysis (diagnoses, procedures, labs, prescriptions)
Fine-tuning Experiments
Adaptation of models to clinical tasks
Impact on performance improvements
Key Findings
Instruction-tuned models' superiority
Challenges in procedure and lab test prediction
Areas for future research
Conclusion
CLIBENCH's contribution to the field
Importance of realistic assessments in healthcare AI
Recommendations for model development and deployment
Basic info
papers
computation and language
machine learning
artificial intelligence
Advanced features
Insights
Which dataset is CLIBENCH derived from?
How does CLIBENCH evaluate large language models in healthcare?
What are the key findings regarding instruction-tuned models in CLIBENCH?
What is the purpose of the CLIBENCH benchmark?

CliBench: Multifaceted Evaluation of Large Language Models in Clinical Decisions on Diagnoses, Procedures, Lab Tests Orders and Prescriptions

Mingyu Derek Ma, Chenchen Ye, Yu Yan, Xiaoxuan Wang, Peipei Ping, Timothy S Chang, Wei Wang·June 14, 2024

Summary

CLIBENCH is a novel benchmark introduced to assess the performance of large language models (LLMs) in clinical decision-making tasks, focusing on diagnoses, procedures, lab tests, and prescriptions. Derived from the MIMIC IV dataset, it offers a comprehensive evaluation by addressing real-world complexity and incorporating diverse medical cases and structured output ontologies. The benchmark evaluates models like GPT-4, LLaMA, and others in zero-shot and fine-tuned settings, revealing strengths and limitations. Key findings show that instruction-tuned models perform better, but there is still room for improvement in tasks like procedure and lab test order predictions. CLIBENCH aims to guide future research on enhancing AI-assisted healthcare through more accurate and realistic assessments.
Mind map
Impact on performance improvements
Adaptation of models to clinical tasks
Task-specific analysis (diagnoses, procedures, labs, prescriptions)
GPT-4 and LLaMA performance comparison
Areas for future research
Challenges in procedure and lab test prediction
Instruction-tuned models' superiority
Fine-tuning Experiments
Zero-shot Setting
Case selection and preprocessing techniques
Structured output ontologies integration
Real-world clinical cases and diversity
MIMIC IV dataset extraction
Guide future research on AI-assisted healthcare
Identify strengths and limitations
To assess LLM performance in clinical tasks
Emergence of large language models in healthcare
[MIMIC IV dataset origin and relevance]
Recommendations for model development and deployment
Importance of realistic assessments in healthcare AI
CLIBENCH's contribution to the field
Key Findings
Model Evaluation
Data Preprocessing
Data Collection
Objective
Background
Conclusion
Methodology
Introduction
Outline
Introduction
Background
[MIMIC IV dataset origin and relevance]
Emergence of large language models in healthcare
Objective
To assess LLM performance in clinical tasks
Identify strengths and limitations
Guide future research on AI-assisted healthcare
Methodology
Data Collection
MIMIC IV dataset extraction
Real-world clinical cases and diversity
Data Preprocessing
Structured output ontologies integration
Case selection and preprocessing techniques
Model Evaluation
Zero-shot Setting
GPT-4 and LLaMA performance comparison
Task-specific analysis (diagnoses, procedures, labs, prescriptions)
Fine-tuning Experiments
Adaptation of models to clinical tasks
Impact on performance improvements
Key Findings
Instruction-tuned models' superiority
Challenges in procedure and lab test prediction
Areas for future research
Conclusion
CLIBENCH's contribution to the field
Importance of realistic assessments in healthcare AI
Recommendations for model development and deployment
Key findings
3

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper aims to evaluate large language models in clinical decisions related to diagnoses, procedures, lab test orders, and prescriptions . It focuses on assessing the performance of these models in making medical decisions based on various clinical data attributes such as patient gender, race, and insurance type . This evaluation is crucial for understanding the capabilities and limitations of language models in the healthcare domain. While the use of language models in clinical decision-making is not a new concept, the specific multifaceted evaluation presented in the paper contributes to advancing the understanding of how these models perform in complex medical scenarios .


What scientific hypothesis does this paper seek to validate?

The paper seeks to validate the scientific hypothesis related to the diagnostic accuracy and supportive capabilities of ChatGPT in prehospital basic life support and pediatric advanced life support cases, as analyzed in the Journal of Medical Systems .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper "CliBench: Multifaceted Evaluation of Large Language Models in Clinical Decisions on Diagnoses, Procedures, Lab Tests Orders and Prescriptions" introduces several innovative aspects in the evaluation of large language models (LLMs) in clinical decision-making .

  1. Task Formulations and Benchmark Design: The benchmark design of CliBench focuses on a comprehensive set of specialties covering various clinical decisions, utilizing large expert ontologies as decision spaces instead of limited answer options. This approach simulates real-world scenarios where options are not predefined, covering a wide range of clinical tasks including diagnosis, procedures, lab test orders, and prescriptions .

  2. Evaluation Set Size and Sampling: The paper discusses the decision to have around 1000 testing instances for comprehensive targets, balancing the coverage of decision categories and benchmark ease of use. The evaluation set is sampled considering the distribution of target decision categories, service departments, and care units. The provided scripts allow practitioners to sample their testing set for a more focused evaluation .

  3. Admission-Based Data Splitting: The clinical tasks considered in the benchmark are admission-based, focusing on understanding the medical records of a specific admission. The data is split into train and test sets based on admissions rather than patients to prevent data leaks during evaluation .

  4. Ontology Statistics and Task Target Candidates: The paper provides statistics on the number of unique candidates at different levels for each clinical decision task, offering insights into the complexity and granularity of decision-making in clinical settings .

  5. Context Lengths of LLMs: The study includes an analysis of the input prompt length distribution for clinical decision tasks, highlighting the varying lengths of patient records and the truncation results statistics for comparing LLMs used in the evaluation .

Overall, the paper presents a multifaceted evaluation framework for LLMs in clinical decision-making, emphasizing the importance of comprehensive benchmarks, ontology-based decision spaces, and tailored evaluation strategies for different clinical tasks . The paper "CliBench: Multifaceted Evaluation of Large Language Models in Clinical Decisions on Diagnoses, Procedures, Lab Tests Orders and Prescriptions" introduces several novel characteristics and advantages compared to previous methods in evaluating large language models (LLMs) in clinical decision-making .

  1. Comprehensive Benchmark Design: CliBench offers a comprehensive benchmark design covering a wide range of clinical tasks, including diagnosis, procedures, lab test orders, and prescriptions. It focuses on a diverse set of specialties and decision categories, providing a more realistic simulation of real-world clinical scenarios .

  2. Ontology-Based Decision Spaces: The benchmark utilizes large expert ontologies as decision spaces, allowing for a broader range of decision options compared to limited predefined answers. This approach enhances the complexity and granularity of decision-making tasks, reflecting the intricacies of clinical practice .

  3. Admission-Based Data Splitting: Unlike previous methods, CliBench adopts an admission-based data splitting strategy for clinical tasks. This approach ensures that data leakage is prevented during evaluation by splitting data into train and test sets based on admissions rather than patients .

  4. Task Target Candidate Statistics: The paper provides detailed statistics on the number of unique candidates at different granular levels for each clinical decision task. This statistical insight offers a deeper understanding of the complexity and diversity of decision-making in clinical settings, enhancing the evaluation process .

  5. Context Length Analysis: The study includes an analysis of the input prompt length distribution for clinical decision tasks, highlighting the varying lengths of patient records and the impact of truncation on LLM performance. This analysis provides valuable insights into the challenges posed by different context lengths in clinical decision-making tasks .

  6. Zero-Shot Evaluation: CliBench conducts zero-shot evaluations of both open-source and closed-source LLMs, revealing their potential and limitations in clinical decision-making. This approach underscores the need for continuous refinement of LLMs to meet the complex demands of real-world clinical diagnoses .

Overall, the characteristics and advantages of CliBench, such as its comprehensive benchmark design, ontology-based decision spaces, admission-based data splitting, detailed task target candidate statistics, context length analysis, and zero-shot evaluations, contribute to a more robust and realistic evaluation framework for LLMs in clinical decision-making compared to previous methods .


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related research studies exist in the field of clinical decision-making using large language models. Noteworthy researchers in this field include Carlos A. Andrade-Castellanos, Ma Teresa Tapia-de la Paz, Pedro E. Farfán-Flores, Rohan Anil, Andrew M. Dai, and many others . These researchers have contributed to the accuracy of diagnostic predictions using language models in the medical domain.

The key to the solution mentioned in the paper involves the comprehensive evaluation and comparison of the capabilities of large language models (LLMs) and LLM-based agent systems in making clinical decisions . The proposed CLIBENCH benchmark aims to simulate real-world clinical decision environments by providing challenging tasks that require domain knowledge, reasoning, generalizability, and expert-ontology understanding to benchmark the development of future LLMs in the medical field.


How were the experiments in the paper designed?

The experiments in the paper were designed to evaluate large language models (LLMs) in clinical decision-making across various tasks such as diagnoses, procedures, lab test orders, and prescriptions. The experiments involved assessing the performance of different LLMs on these clinical decision tasks based on factors like input prompt length distribution, context lengths, number of unique candidates, and ICD-10-CM chapters . The experiments also compared the performance of LLMs with varying parameters and sizes, such as LLaMA3, GPT-3.5 turbo, GPT-4 turbo, and GPT-4o, to analyze their effectiveness in clinical decision support . Additionally, the experiments analyzed the diagnostic capabilities of the models concerning patient attributes like gender, race, and insurance type to understand the models' performance variations based on these factors .


What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study is CLIBENCH, which is a benchmark developed from the MIMIC IV dataset . The code and data for CLIBENCH are open source and available on GitHub at the following link: github.com/clibench/clibench .


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide substantial support for the scientific hypotheses that require verification. The study conducted a multifaceted evaluation of large language models in clinical decisions on diagnoses, procedures, lab test orders, and prescriptions . The research involved various models like ChatGPT, Mistral, BioMistral, and LLaMA3, among others, to assess their performance in clinical decision-making tasks . These models were evaluated based on parameters such as precision, recall, and F1 score, providing a comprehensive analysis of their capabilities in different medical scenarios .

The paper's findings demonstrate the accuracy and effectiveness of these large language models in assisting with clinical tasks, including diagnosis prediction, procedure decision-making, and prescription recommendations . The models showed promising results in generating accurate differential diagnoses early in emergency department presentations and supporting lay rescuers in prehospital basic life support and pediatric advanced life support cases . Additionally, the models exhibited strong performance in radiological image analysis and clinical vignette generation .

Overall, the experiments and results outlined in the paper offer robust evidence supporting the efficacy and potential of large language models in enhancing clinical decision-making processes across various medical domains. The detailed evaluation of these models in real-world healthcare scenarios underscores their utility and reliability in assisting healthcare professionals with critical tasks, thereby validating the scientific hypotheses under investigation .


What are the contributions of this paper?

The paper makes several contributions in the field of clinical decision-making using large language models (LLMs) . These contributions include:

  • Development of a benchmark: The paper introduces CLIBENCH, a benchmark designed to evaluate and compare the practical clinical knowledge of LLMs and LLM-based agent systems .
  • Simulation of real-world clinical decision environment: CLIBENCH aims to simulate real-world clinical decision-making scenarios using accessible clinical data, such as diagnosis decisions, procedure decisions, lab test orders, and prescriptions .
  • Focus on diagnosis decision tasks: The benchmark focuses on tasks like identifying a diagnosis based on patient medical records during their stay, providing a challenging set of tasks that require domain knowledge, reasoning, and generalizability .
  • Zero-shot setting: The paper emphasizes the use of a zero-shot setting for clinical decision tasks in CLIBENCH due to the comprehensive nature of input patient records and the diversity of clinical cases .
  • Exclusion of temporal predictive models: Unlike temporal predictive models, the tasks in CLIBENCH do not include patient history information in the input, except for last-admission diagnosis codes for the diagnosis decision task, focusing on the patient records of the current admission .

What work can be continued in depth?

To delve deeper into the research, further exploration can be conducted on the following aspects:

  • Performance vs task difficulties: Investigating the impact of patient length of stay on the recall and precision of diagnosis decisions made by models like GPT-4o and LLaMA3 70B Instruct .
  • Performance vs diversity of diagnoses: Analyzing how the performance of models varies based on the number of unique diagnosis chapters involved in the ground-truth billing code, which reflects the diversity and scope of diagnoses .
  • Performance vs number of ground-truth diagnoses: Studying the performance trends for admission instances with different numbers of ground-truth diagnoses to understand how the number of diagnoses affects model performance .
  • Truncation rules: Further exploring the impact of truncation rules on the input prompt length distribution for clinical decision tasks and how it influences the performance of large language models .
  • Prescriptions decision-making performance: Delving into the detailed performance analysis of various models in making prescription decisions, considering parameters like precision, recall, and F1 scores across different levels .
  • ICD-10-CM chapters: Examining the code blocks and titles for different chapters in the International Statistical Classification of Diseases and Related Health Problems (ICD-10) to understand how these chapters relate to clinical decision-making tasks .
Tables
9
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