Characterizing Personal Clinical Cognitive Uncertainty and Its Association With Clinical Judgment
Bibliographic record
Abstract
RATIONALE: Clinical uncertainty has been studied mostly in relation to clinical scenarios or as a personal characteristic. The intersection between clinical context and personal characteristics remains underexplored. Specific forms of personal and cognitive clinical uncertainty may exist at different layers of cognitive processes, and impact clinical judgment and training. AIMS AND OBJECTIVES: We aimed to characterize and quantify facets of personal clinical cognitive uncertainty, and examine associations with clinical judgment. METHODS: We recruited 120 learners and 24 supervisors at the Centre hospitalier de l'Université de Montréal. Learners completed 15 multiple-choice vignettes and recorded their level of uncertainty for each. Learners' characteristics and supervisor-rated clinical judgment were compiled. We quantified five uncertainty measures: self-reported general uncertainty, degree of clinical uncertainty, relative, absolute, and objective calibrations of uncertainty. Correlations between demographic characteristics, test scores, and uncertainty measures were computed. We examined adjusted linear regressions of clinical judgment on uncertainty measures. RESULTS: Mean age of learners was 24.9 years (SD = 3.5), 80 (68%) were women, 52 (44%) had undergraduate education. Mean test score was 61% (13) and supervisor-rated clinical judgment was 40 (10) over 60. Higher degree of clinical uncertainty correlated with lower scores, lower training levels, and being a woman. Higher self-reported general uncertainty was associated with higher degree of clinical uncertainty but lower relative and absolute calibrations. Higher test score was correlated with higher absolute and objective calibrations. Lower self-reported general uncertainty (standardized β = -0.27, p = 0.003), lower degree of uncertainty (β = -0.27, p = 0.01), higher relative (β = 0.16, p = 0.09) and absolute calibrations (β = 0.18, p = 0.06) were associated with clinical judgment. CONCLUSION: We identified five measures of personal clinical cognitive uncertainty with differential associations with clinical judgment and knowledge. Greater focus on understanding and teaching of personal clinical cognitive uncertainty may enhance clinicians' tolerance to uncertainty and improve clinical judgment and outcomes.
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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 | 0.009 | 0.097 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".