Clinical Reasoning Tasks and Resident Physicians: What Do They Reason About?
Bibliographic record
Abstract
PURPOSE: A framework of clinical reasoning tasks thought to occur in a clinical encounter was recently developed. It proposes that diagnostic and therapeutic reasoning comprise 24 tasks. The authors of this current study used this framework to investigate what internal medicine residents reason about when they approach straightforward clinical cases. METHOD: Participants viewed three video-recorded clinical encounters portraying common diagnoses. After each video, participants completed a post encounter form and think-aloud protocol. Two authors analyzed transcripts from the think-aloud protocols using a constant comparative approach. They conducted iterative coding of the utterances, classifying each according to the framework of clinical reasoning tasks. They evaluated the type, number, and sequence of tasks the residents used. RESULTS: Ten residents participated in the study in 2013-2014. Across all three cases, the residents employed 14 clinical reasoning tasks. Nearly all coded tasks were associated with framing the encounter or diagnosis. The order in which residents used specific tasks varied. The average number of tasks used per case was as follows: Case 1, 4.4 (range 1-10); Case 2, 4.6 (range 1-6); and Case 3, 4.7 (range 1-7). The residents used some tasks repeatedly; the average number of task utterances was 11.6, 13.2, and 14.7 for, respectively, Case 1, 2, and 3. CONCLUSIONS: Results suggest that the use of clinical reasoning tasks occurs in a varied, not sequential, process. The authors provide suggestions for strengthening the framework to more fully encompass the spectrum of reasoning tasks that occur in residents' clinical encounters.
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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 | 0.004 | 0.121 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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".