Understanding Clinical Uncertainty
Bibliographic record
Abstract
PURPOSE: In clinical settings, uncertainty is part of everyday practice. However, a lack of insight into how experts approach uncertainty limits the ability to explicitly teach and assess it in training. This study explored how experienced surgeons perceived and handled uncertainty during challenging intraoperative situations, to develop a theoretical language supporting both education and research. METHOD: This constructivist qualitative study included observations and interviews during 26 surgical cases. The cases, drawn from seven staff surgeons from various specialties at a medical school, were purposively sampled after being preidentified by the surgeon as "likely challenging." The authors combined template and inductive analyses. In template analysis, an existing theory was used to identify instances of uncertainty in the dataset. Inductive analysis was used to elaborate and refine the concepts. RESULTS: Template analysis confirmed that existing theoretical concepts are relevant to surgery. However, inductive analysis revealed additional concepts and positioned existing concepts within new relationships. Two new theoretical themes were recognizing uncertainty and responding to uncertainty, each with corresponding subthemes. Factors such as the novelty of the situation, difficulty in predicting the outcome, and difficulty deciding the course of action mainly characterize an uncertain situation in surgery according to the participants. CONCLUSIONS: The results offer a refined language for conceptualizing uncertainty in surgery. Although further research could elaborate and test the explanatory power of this language, the authors anticipate that it has implications both for current discussions of surgical safety and for future development of explicit training for effective management of surgical uncertainty.
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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 | 0.024 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.005 | 0.030 |
| Scholarly communication | 0.012 | 0.017 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".