Resident Physician Recognition of Tachypnea in Clinical Simulation Videos in Japan: Cross-Sectional Study
Bibliographic record
Abstract
Background: Traditional assessments of clinical competence using multiple-choice questions (MCQs) have limitations in the evaluation of real-world diagnostic abilities. As such, recognizing non-verbal cues, like tachypnea, is crucial for accurate diagnosis and effective patient care. Objective: This study aimed to evaluate how detecting such cues impacts the clinical competence of resident physicians by using a clinical simulation video integrated into the General Medicine In-Training Examination (GM-ITE). Methods: This multicenter cross-sectional study enrolled first- and second-year resident physicians who participated in the GM-ITE 2022. Participants watched a 5-minute clinical simulation video depicting a patient with acute pulmonary thromboembolism, and subsequently answered diagnostic questions. Propensity score matching was applied to create balanced groups of resident physicians who detected tachypnea (ie, the detection group) and those who did not (ie, the non-detection group). After matching, we compared the GM-ITE scores and the proportion of correct clinical simulation video answers between the two groups. Subgroup analyses assessed the consistency between results. Results: In total, 5105 resident physicians were included, from which 959 pairs were identified after the clinical simulation video. Covariates were well balanced between the detection and non-detection groups (standardized mean difference <0.1 for all variables). Post-matching, the detection group achieved significantly higher GM-ITE scores (mean [SD], 47.6 [8.4]) than the non-detection group (mean [SD], 45.7 [8.1]; mean difference, 1.9; 95% CI, 1.1-2.6; P=.041). The proportion of correct clinical simulation video answers was also significantly higher in the detection group (39.2% vs 3.0%; mean difference, 36.2%; 95% CI, 32.8-39.4). Subgroup analyses confirmed consistent results across sex, postgraduate years, and age groups. Conclusions: Overall, this study revealed that detecting non-verbal cues like tachypnea significantly affects clinical competence, as evidenced by higher GM-ITE scores among resident physicians. Integrating video-based simulations into traditional MCQ examinations enhances the assessment of diagnostic skills by providing a more comprehensive evaluation of clinical abilities. Thus, recognizing non-verbal cues is crucial for clinical competence. Video-based simulations offer a valuable addition to traditional knowledge assessments by improving the diagnostic skills and preparedness of clinicians.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".