The MedPerturb Dataset: What Non-Content Perturbations Reveal About Human and Clinical LLM Decision Making | Paper Citation Record

Observation 9e4ffd32-3ebf-4699-982f-5fcfb0c4ded6 · outbound

This paper cites Evaluating large language models on medical evidence summarization.

The MedPerturb Dataset: What Non-Content Perturbations Reveal About Human and Clinical LLM Decision Making Evaluating large language models on medical evidence summarization

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This paper cites Adapted large language models can outperform medical experts in clinical text summarization.

The MedPerturb Dataset: What Non-Content Perturbations Reveal About Human and Clinical LLM Decision Making Adapted large language models can outperform medical experts in clinical text summarization

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This paper cites Evaluating LLM -- Generated Multimodal Diagnosis from Medical Images and Symptom Analysis.

The MedPerturb Dataset: What Non-Content Perturbations Reveal About Human and Clinical LLM Decision Making Evaluating LLM -- Generated Multimodal Diagnosis from Medical Images and Symptom Analysis

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The MedPerturb Dataset: What Non-Content Perturbations Reveal About Human and Clinical LLM Decision Making Llm-based agentic systems in medicine and healthcare

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This paper cites MedDM:LLM-executable clinical guidance tree for clinical decision-making.

The MedPerturb Dataset: What Non-Content Perturbations Reveal About Human and Clinical LLM Decision Making MedDM:LLM-executable clinical guidance tree for clinical decision-making

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The MedPerturb Dataset: What Non-Content Perturbations Reveal About Human and Clinical LLM Decision Making Large language models encode clinical knowledge

Reference 6

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This paper cites Toward expert-level medical question answering with large language models.

The MedPerturb Dataset: What Non-Content Perturbations Reveal About Human and Clinical LLM Decision Making Toward expert-level medical question answering with large language models

Reference 7

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This paper cites Luks and Zachary D.

The MedPerturb Dataset: What Non-Content Perturbations Reveal About Human and Clinical LLM Decision Making Luks and Zachary D

Reference 8

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This paper cites Variability in language used on social media prior to hospital visits.

The MedPerturb Dataset: What Non-Content Perturbations Reveal About Human and Clinical LLM Decision Making Variability in language used on social media prior to hospital visits

Reference 9

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This paper cites How does chatgpt perform on the united states medical licensing examination (usmle)? the implications of large language models for medical education and knowledge assessment.

The MedPerturb Dataset: What Non-Content Perturbations Reveal About Human and Clinical LLM Decision Making How does chatgpt perform on the united states medical licensing examination (usmle)? the implications of large language models for medical education and knowledge assessment

Reference 10

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The MedPerturb Dataset: What Non-Content Perturbations Reveal About Human and Clinical LLM Decision Making Open medical llm leaderboard

Reference 11

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This paper cites A rapid review of gender, sex, and sexual orientation documentation in electronic health records.

The MedPerturb Dataset: What Non-Content Perturbations Reveal About Human and Clinical LLM Decision Making A rapid review of gender, sex, and sexual orientation documentation in electronic health records

Reference 12

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This paper cites Health Care Experiences of Patients with Nonbinary Gender Identities.

The MedPerturb Dataset: What Non-Content Perturbations Reveal About Human and Clinical LLM Decision Making Health Care Experiences of Patients with Nonbinary Gender Identities

Reference 13

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This paper cites Hoffmann, Roger B.

The MedPerturb Dataset: What Non-Content Perturbations Reveal About Human and Clinical LLM Decision Making Hoffmann, Roger B

Reference 14

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The MedPerturb Dataset: What Non-Content Perturbations Reveal About Human and Clinical LLM Decision Making Unresolved cited work

Reference 15

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Observation df9834ba-ed33-4765-be03-b5b8f5a6d95f · outbound

This paper cites Gender disparities in health care.

The MedPerturb Dataset: What Non-Content Perturbations Reveal About Human and Clinical LLM Decision Making Gender disparities in health care

Reference 16

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The MedPerturb Dataset: What Non-Content Perturbations Reveal About Human and Clinical LLM Decision Making Defining gender disparities in pain management

Reference 17

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This paper cites Gender differences in outcomes of a multimodal pain management program.

The MedPerturb Dataset: What Non-Content Perturbations Reveal About Human and Clinical LLM Decision Making Gender differences in outcomes of a multimodal pain management program

Reference 18

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Observation 9b8a5ea0-c979-47fc-b5b8-eeba7f4a7354 · outbound

This paper cites Health and healthcare disparities among us women and men at the intersection of sexual orientation and race/ethnicity: a nationally representative cross-sectional study.

The MedPerturb Dataset: What Non-Content Perturbations Reveal About Human and Clinical LLM Decision Making Health and healthcare disparities among us women and men at the intersection of sexual orientation and race/ethnicity: a nationally representative cross-sectional study

Reference 19

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This paper cites Gender bias in transformers: A comprehensive review of detection and mitigation strategies.

The MedPerturb Dataset: What Non-Content Perturbations Reveal About Human and Clinical LLM Decision Making Gender bias in transformers: A comprehensive review of detection and mitigation strategies

Reference 20

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This paper cites Gender bias in natural language processing and computer vision: A comparative survey.

The MedPerturb Dataset: What Non-Content Perturbations Reveal About Human and Clinical LLM Decision Making Gender bias in natural language processing and computer vision: A comparative survey

Reference 21

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This paper cites Vision-Language Models Performing Zero-Shot Tasks Exhibit Gender-based Disparities.

The MedPerturb Dataset: What Non-Content Perturbations Reveal About Human and Clinical LLM Decision Making Vision-Language Models Performing Zero-Shot Tasks Exhibit Gender-based Disparities

Reference 22

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The MedPerturb Dataset: What Non-Content Perturbations Reveal About Human and Clinical LLM Decision Making Addressing gender-related performance disparities in neural rankers

Reference 23

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The MedPerturb Dataset: What Non-Content Perturbations Reveal About Human and Clinical LLM Decision Making Evaluating Bias and Fairness in Gender-Neutral Pretrained Vision-and-Language Models

Reference 24

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This paper cites Bias in bios: A case study of semantic representation bias in a high-stakes setting.

The MedPerturb Dataset: What Non-Content Perturbations Reveal About Human and Clinical LLM Decision Making Bias in bios: A case study of semantic representation bias in a high-stakes setting

Reference 25

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This paper cites Assessing the potential of gpt-4 to perpetuate racial and gender biases in health care: a model evaluation study.

The MedPerturb Dataset: What Non-Content Perturbations Reveal About Human and Clinical LLM Decision Making Assessing the potential of gpt-4 to perpetuate racial and gender biases in health care: a model evaluation study

Reference 26

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This paper cites Bias patterns in the application of LLMs for clinical decision support: A comprehensive study.

The MedPerturb Dataset: What Non-Content Perturbations Reveal About Human and Clinical LLM Decision Making Bias patterns in the application of LLMs for clinical decision support: A comprehensive study

Reference 27

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This paper cites An investigation into the impact of deep learning model choice on sex and race bias in cardiac mr segmentation.

The MedPerturb Dataset: What Non-Content Perturbations Reveal About Human and Clinical LLM Decision Making An investigation into the impact of deep learning model choice on sex and race bias in cardiac mr segmentation

Reference 28

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The MedPerturb Dataset: What Non-Content Perturbations Reveal About Human and Clinical LLM Decision Making Sex and gender differences and biases in artificial intelligence for biomedicine and healthcare

Reference 29

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The MedPerturb Dataset: What Non-Content Perturbations Reveal About Human and Clinical LLM Decision Making Algorithmic fairness and bias mitigation for clinical machine learning with deep reinforcement learning

Reference 30

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The MedPerturb Dataset: What Non-Content Perturbations Reveal About Human and Clinical LLM Decision Making Subbalakshmi

Reference 31

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The MedPerturb Dataset: What Non-Content Perturbations Reveal About Human and Clinical LLM Decision Making Gender identification from e-mails

Reference 32

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The MedPerturb Dataset: What Non-Content Perturbations Reveal About Human and Clinical LLM Decision Making Gender, pseudonyms, and cmc: Masking identities and baring souls

Reference 33

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The MedPerturb Dataset: What Non-Content Perturbations Reveal About Human and Clinical LLM Decision Making Bensing, W

Reference 34

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This paper cites Write it like you see it: De- tectable differences in clinical notes by race lead to differential model recommendations.

The MedPerturb Dataset: What Non-Content Perturbations Reveal About Human and Clinical LLM Decision Making Write it like you see it: De- tectable differences in clinical notes by race lead to differential model recommendations

Reference 35

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The MedPerturb Dataset: What Non-Content Perturbations Reveal About Human and Clinical LLM Decision Making Peek, and Elizabeth L

Reference 36

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The MedPerturb Dataset: What Non-Content Perturbations Reveal About Human and Clinical LLM Decision Making "Kelly is a Warm Person, Joseph is a Role Model": Gender Biases in LLM-Generated Reference Letters

Reference 37

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The MedPerturb Dataset: What Non-Content Perturbations Reveal About Human and Clinical LLM Decision Making How Individual Traits and Language Styles Shape Preferences In Open-ended User-LLM Interaction: A Preliminary Study

Reference 38

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The MedPerturb Dataset: What Non-Content Perturbations Reveal About Human and Clinical LLM Decision Making Closing the gap between open source and commercial large language models for medical evidence summarization

Reference 39

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The MedPerturb Dataset: What Non-Content Perturbations Reveal About Human and Clinical LLM Decision Making Conversational ai in health: Design considerations from a wizard-of-oz dermatology case study with users, clinicians and a medical llm

Reference 40

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The MedPerturb Dataset: What Non-Content Perturbations Reveal About Human and Clinical LLM Decision Making Guidelines for rigorous evaluation of clinical llms for conversational reasoning

Reference 41

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This paper cites Effectiveness of a chatbot for eating disorders prevention: a randomized clinical trial.

The MedPerturb Dataset: What Non-Content Perturbations Reveal About Human and Clinical LLM Decision Making Effectiveness of a chatbot for eating disorders prevention: a randomized clinical trial

Reference 42

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The MedPerturb Dataset: What Non-Content Perturbations Reveal About Human and Clinical LLM Decision Making Performance of chatgpt on free-response, clinical reasoning exams

Reference 43

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The MedPerturb Dataset: What Non-Content Perturbations Reveal About Human and Clinical LLM Decision Making The next generation: chatbots in clinical psychology and psychotherapy to foster mental health–a scoping review

Reference 44

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The MedPerturb Dataset: What Non-Content Perturbations Reveal About Human and Clinical LLM Decision Making Large language model influence on diagnostic reasoning: a randomized clinical trial

Reference 45

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The MedPerturb Dataset: What Non-Content Perturbations Reveal About Human and Clinical LLM Decision Making Human-algorithmic interaction using a large language model-augmented artificial intelligence clinical decision support system

Reference 46

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This paper cites The impact of responding to patient messages with large language model assistance.

The MedPerturb Dataset: What Non-Content Perturbations Reveal About Human and Clinical LLM Decision Making The impact of responding to patient messages with large language model assistance

Reference 47

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The MedPerturb Dataset: What Non-Content Perturbations Reveal About Human and Clinical LLM Decision Making Com- paring physician and artificial intelligence chatbot responses to patient questions posted to a public social media forum

Reference 48

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The MedPerturb Dataset: What Non-Content Perturbations Reveal About Human and Clinical LLM Decision Making An evaluation framework for clinical use of large language models in patient interaction tasks

Reference 49

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The MedPerturb Dataset: What Non-Content Perturbations Reveal About Human and Clinical LLM Decision Making The medium is the message: How non-clinical information shapes clinical decisions in llms

Reference 50

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This paper cites Chatgpt: the next-gen tool for triaging? The American journal of emergency medicine, 69:215–217, 2023.

The MedPerturb Dataset: What Non-Content Perturbations Reveal About Human and Clinical LLM Decision Making Chatgpt: the next-gen tool for triaging? The American journal of emergency medicine, 69:215–217, 2023

Reference 51

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This paper cites The diagnostic and triage accuracy of the gpt-3 artificial intelligence model.

The MedPerturb Dataset: What Non-Content Perturbations Reveal About Human and Clinical LLM Decision Making The diagnostic and triage accuracy of the gpt-3 artificial intelligence model

Reference 52

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This paper cites Triage performance across large language models, chatgpt, and untrained doctors in emergency medicine: comparative study.

The MedPerturb Dataset: What Non-Content Perturbations Reveal About Human and Clinical LLM Decision Making Triage performance across large language models, chatgpt, and untrained doctors in emergency medicine: comparative study

Reference 53

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This paper cites Evaluating llm-based generative ai tools in emergency triage: A comparative study of chatgpt plus, copilot pro, and triage nurses.

The MedPerturb Dataset: What Non-Content Perturbations Reveal About Human and Clinical LLM Decision Making Evaluating llm-based generative ai tools in emergency triage: A comparative study of chatgpt plus, copilot pro, and triage nurses

Reference 54

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This paper cites Integration of customised llm for discharge summary generation in real-world clinical settings: a pilot study on russell gpt.

The MedPerturb Dataset: What Non-Content Perturbations Reveal About Human and Clinical LLM Decision Making Integration of customised llm for discharge summary generation in real-world clinical settings: a pilot study on russell gpt

Reference 55

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The MedPerturb Dataset: What Non-Content Perturbations Reveal About Human and Clinical LLM Decision Making A toolbox for surfacing health equity harms and biases in large language models

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Resolution: unresolved; no resolver link, observed 2026-08-15T19:15:21.894779Z

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Observation 793693f4-dc90-4b61-af2a-c9f7fb331166 · outbound

This paper cites Can AI Relate: Testing Large Language Model Response for Mental Health Support.

The MedPerturb Dataset: What Non-Content Perturbations Reveal About Human and Clinical LLM Decision Making Can AI Relate: Testing Large Language Model Response for Mental Health Support

Reference 57

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Observation b9b7e40d-e11e-4c60-99bf-3baec94930de · outbound

This paper cites A systematic review of large language model (llm) evaluations in clinical medicine.

The MedPerturb Dataset: What Non-Content Perturbations Reveal About Human and Clinical LLM Decision Making A systematic review of large language model (llm) evaluations in clinical medicine

Reference 58

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This paper cites Evaluating the clinical benefits of llms.Nature Medicine, 30(9):2409–2410, 2024.

The MedPerturb Dataset: What Non-Content Perturbations Reveal About Human and Clinical LLM Decision Making Evaluating the clinical benefits of llms.Nature Medicine, 30(9):2409–2410, 2024

Reference 59

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This paper cites What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams.

The MedPerturb Dataset: What Non-Content Perturbations Reveal About Human and Clinical LLM Decision Making What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams

Reference 60

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The MedPerturb Dataset: What Non-Content Perturbations Reveal About Human and Clinical LLM Decision Making PubMedQA: A Dataset for Biomedical Research Question Answering

Reference 61

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This paper cites DiversityMedQA: A benchmark for assessing demographic biases in medical diagnosis using large language models.

The MedPerturb Dataset: What Non-Content Perturbations Reveal About Human and Clinical LLM Decision Making DiversityMedQA: A benchmark for assessing demographic biases in medical diagnosis using large language models

Reference 62

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The MedPerturb Dataset: What Non-Content Perturbations Reveal About Human and Clinical LLM Decision Making MEDIC: Comprehensive Evaluation of Leading Indicators for LLM Safety and Utility in Clinical Applications

Reference 63

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This paper cites Performance of large language models on medical oncology examination questions.

The MedPerturb Dataset: What Non-Content Perturbations Reveal About Human and Clinical LLM Decision Making Performance of large language models on medical oncology examination questions

Reference 64

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The MedPerturb Dataset: What Non-Content Perturbations Reveal About Human and Clinical LLM Decision Making Medical Large Language Model Benchmarks Should Prioritize Construct Validity

Reference 65

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The MedPerturb Dataset: What Non-Content Perturbations Reveal About Human and Clinical LLM Decision Making Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena

Reference 66

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The MedPerturb Dataset: What Non-Content Perturbations Reveal About Human and Clinical LLM Decision Making Systematic Evaluation of LLM-as-a-Judge in LLM Alignment Tasks: Explainable Metrics and Diverse Prompt Templates

Reference 67

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This paper cites Automating evaluation of ai text generation in healthcare with a large language model (llm)-as- a-judge.

The MedPerturb Dataset: What Non-Content Perturbations Reveal About Human and Clinical LLM Decision Making Automating evaluation of ai text generation in healthcare with a large language model (llm)-as- a-judge

Reference 68

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The MedPerturb Dataset: What Non-Content Perturbations Reveal About Human and Clinical LLM Decision Making Limitations of the llm-as-a-judge approach for evaluating llm outputs in expert knowledge tasks

Reference 69

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Observation 9950664d-9951-4067-b49c-804a0c7f3f1f · outbound

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The MedPerturb Dataset: What Non-Content Perturbations Reveal About Human and Clinical LLM Decision Making A Survey on LLM-as-a-Judge

Reference 70

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The MedPerturb Dataset: What Non-Content Perturbations Reveal About Human and Clinical LLM Decision Making Can Large Language Models Be an Alternative to Human Evaluations?

Reference 71

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The MedPerturb Dataset: What Non-Content Perturbations Reveal About Human and Clinical LLM Decision Making Justice or Prejudice? Quantifying Biases in LLM-as-a-Judge

Reference 72

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The MedPerturb Dataset: What Non-Content Perturbations Reveal About Human and Clinical LLM Decision Making Judging the Judges: Evaluating Alignment and Vulnerabilities in LLMs-as-Judges

Reference 73

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The MedPerturb Dataset: What Non-Content Perturbations Reveal About Human and Clinical LLM Decision Making As- sessment of pathology domain-specific knowledge of chatgpt and comparison to human performance

Reference 74

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The MedPerturb Dataset: What Non-Content Perturbations Reveal About Human and Clinical LLM Decision Making Quality of answers of generative large language models versus peer users for interpreting laboratory test results for lay patients: evaluation study

Reference 75

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The MedPerturb Dataset: What Non-Content Perturbations Reveal About Human and Clinical LLM Decision Making Style Over Substance: Evaluation Biases for Large Language Models

Reference 76

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The MedPerturb Dataset: What Non-Content Perturbations Reveal About Human and Clinical LLM Decision Making Ehrnoteqa: An llm benchmark for real- world clinical practice using discharge summaries

Reference 77

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The MedPerturb Dataset: What Non-Content Perturbations Reveal About Human and Clinical LLM Decision Making Tran, Daniel I Schlessinger, Shannon Wongvibulsin, Zhuo Ran Cai, Roxana Daneshjou, and Pranav Rajpurkar

Reference 78

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The MedPerturb Dataset: What Non-Content Perturbations Reveal About Human and Clinical LLM Decision Making The Llama 3 Herd of Models

Reference 79

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The MedPerturb Dataset: What Non-Content Perturbations Reveal About Human and Clinical LLM Decision Making Linguistic analy- sis of communication in therapist-assisted internet-delivered cognitive behavior therapy for generalized anxiety disorder

Reference 80

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The MedPerturb Dataset: What Non-Content Perturbations Reveal About Human and Clinical LLM Decision Making Toward linguistic recognition of generalized anxiety disorder

Reference 81

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The MedPerturb Dataset: What Non-Content Perturbations Reveal About Human and Clinical LLM Decision Making Linguistic markers of anxiety and depression in somatic symptom and related disorders: Observational study of a digital intervention

Reference 82

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The MedPerturb Dataset: What Non-Content Perturbations Reveal About Human and Clinical LLM Decision Making Are patient linguistic tones associated with mental health and perceived clinician empathy? JBJS, 103(23):2181–2189, 2021

Reference 83

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The MedPerturb Dataset: What Non-Content Perturbations Reveal About Human and Clinical LLM Decision Making GPT-4 Technical Report

Reference 84

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The MedPerturb Dataset: What Non-Content Perturbations Reveal About Human and Clinical LLM Decision Making Palmyra-med: Instruction-based fine-tuning of llms enhancing medical domain performance

Reference 85

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The MedPerturb Dataset: What Non-Content Perturbations Reveal About Human and Clinical LLM Decision Making The equivalence of weighted kappa and the intraclass correlation coefficient as measures of reliability

Reference 86

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The MedPerturb Dataset: What Non-Content Perturbations Reveal About Human and Clinical LLM Decision Making Multiple significance tests: the bonferroni method

Reference 87

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The MedPerturb Dataset: What Non-Content Perturbations Reveal About Human and Clinical LLM Decision Making Note on the sampling error of the difference between correlated proportions or percentages

Reference 88

Resolution: unresolved; no resolver link, observed 2026-08-15T19:15:22.008516Z

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The MedPerturb Dataset: What Non-Content Perturbations Reveal About Human and Clinical LLM Decision Making Individual comparisons by ranking methods

Reference 89

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The MedPerturb Dataset: What Non-Content Perturbations Reveal About Human and Clinical LLM Decision Making The measurement of observer agreement for categorical data

Reference 90

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The MedPerturb Dataset: What Non-Content Perturbations Reveal About Human and Clinical LLM Decision Making On the use and interpretation of certain test criteria for purposes of statistical inference part i

Reference 91

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The MedPerturb Dataset: What Non-Content Perturbations Reveal About Human and Clinical LLM Decision Making On a test of whether one of two random variables is stochastically larger than the other

Reference 92

Resolution: unresolved; no resolver link, observed 2026-08-15T19:15:22.021993Z

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The MedPerturb Dataset: What Non-Content Perturbations Reveal About Human and Clinical LLM Decision Making Gender bias and stereotypes in large language models

Reference 93

Resolution: unresolved; no resolver link, observed 2026-08-15T19:15:22.024908Z

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The MedPerturb Dataset: What Non-Content Perturbations Reveal About Human and Clinical LLM Decision Making Llm evaluators recognize and favor their own generations

Reference 94

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The MedPerturb Dataset: What Non-Content Perturbations Reveal About Human and Clinical LLM Decision Making Bender and Batya Friedman

Reference 95

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The MedPerturb Dataset: What Non-Content Perturbations Reveal About Human and Clinical LLM Decision Making Basic demographics, health practices, and health status of us medical students

Reference 96

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The MedPerturb Dataset: What Non-Content Perturbations Reveal About Human and Clinical LLM Decision Making International medical graduates in the us physician workforce and graduate medical education: current and historical trends

Reference 97

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The MedPerturb Dataset: What Non-Content Perturbations Reveal About Human and Clinical LLM Decision Making Clinical reasoning education at us medical schools: results from a national survey of internal medicine clerkship directors

Reference 98

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The MedPerturb Dataset: What Non-Content Perturbations Reveal About Human and Clinical LLM Decision Making Teaching medical students the important connection between communication and clinical reasoning

Reference 99

Resolution: unresolved; no resolver link, observed 2026-08-15T19:15:22.046929Z

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The MedPerturb Dataset: What Non-Content Perturbations Reveal About Human and Clinical LLM Decision Making Factors associated with medical student clinical reasoning and evidence based medicine practice

Reference 100

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