Transforming Medical Assessment: Integrating Uncertainty Into the Evaluation of Clinical Reasoning in Medical Education
Bibliographic record
Abstract
In an age where practicing physicians have access to an overwhelming volume of clinical information and are faced with increasingly complex medical decisions, the ability to execute sound clinical reasoning is essential to optimal patient care. The authors propose two concepts that are philosophically paramount to the future assessment of clinical reasoning in medicine: assessment in the context of "uncertainty" (when, despite all of the information that is available, there is still significant doubt as to the best diagnosis, investigation, or treatment), and acknowledging that it is entirely possible (and reasonable) to have more than "one correct answer." The purpose of this article is to highlight key elements related to these two core concepts and discuss genuine barriers that currently exist on the pathway to creating such assessments. These include acknowledging situations of uncertainty, creating clear frameworks that define progressive levels of clinical reasoning skills, providing validity evidence to increase the defensibility of such assessments, considering the comparative feasibility with other forms of assessment, and developing strategies to evaluate the impact of these assessment methods on future learning and practice. The authors recommend that concerted efforts be directed toward these key areas to help advance the field of clinical reasoning assessment, improve the clinical care decisions made by current and future physicians, and have positive outcomes for patients. It is anticipated that these and subsequent efforts will aid in reaching the goal of making future assessment in medical education more representative of current-day clinical reasoning and decision making.
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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 | 0.097 | 0.205 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.004 | 0.033 |
| Scholarly communication | 0.024 | 0.028 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".