Diagnostic conversations: Clinical Decision Making in surgery – Part 2
Bibliographic record
Abstract
Proceduralists who fail to review their decision making are unlikely to learn from their experiences, irrespective of whether the operative outcome is successful or not. Teaching junior surgeons to develop 'insight' into their own decision making has long been a challenge. Surgeons and staff of the Royal Australasian College of Surgeons worked together to develop a model to help explain the processes around clinical decision making and incorporated this model into a Clinical Decision Making (CDM) training course. In this course, faculty apply the model to specific surgical cases, within the model's framework of how clinical decisions are made; thus providing an opportunity to identify specific decision making processes as they occur and to highlight some of the learning opportunities they provide. The conversation in this paper illustrates the kinds of case-based interactions which typically occur in the development and teaching of the CDM course.The focus in this, the second of two papers, is on reviewing post-operative clinical decisions made in relation to one case, to improve the quality of subsequent 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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".