Developing a Unified List of Physicians’ Reasoning Tasks During Clinical Encounters
Bibliographic record
Abstract
PURPOSE: The clinical reasoning literature focuses on how physicians reason while making decisions, rather than on what they reason about while performing their clinical tasks. In an attempt to provide a common language for discussing, teaching, and researching clinical reasoning, the authors undertook the task of developing a unified list of physicians' reasoning tasks, or what they reason about, during clinical encounters. METHOD: The authors compiled an initial list of 20 reasoning tasks based on the literature from four content areas--clinical reasoning, communications, medical errors, and clinical guidelines. In the summer and fall of 2010, they surveyed a purposive sample of 46 international experts in clinical reasoning and communications. From the results of the first survey, the authors refined their list of reasoning tasks, then resurveyed 22 of the original participants. From the results of the second survey, they further refined their list and validated the inclusion of the reasoning tasks. RESULTS: Twenty-four of 46 (52%) and 15 of 22 (65%) participants completed the first- and second-round surveys, respectively. Following the second-round survey, the authors' list included 24 reasoning tasks, and a clinical example corresponding to each, that fell into four broad categories: framing the encounter (3), diagnosis (8), management (11), and self-reflection (2). CONCLUSIONS: The development of this unified list represents a first step in offering a vocabulary for discussing, reflecting on, teaching, and studying physicians' reasoning tasks during clinical encounters.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.042 | 0.121 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".