Error Detection–Based Model to Assess Educational Outcomes in Crisis Resource Management Training: A Pilot Study
Bibliographic record
Abstract
Otolaryngology-head and neck surgery (OTL-HNS) residents face a variety of difficult, high-stress situations, which may occur early in their training. Since these events occur infrequently, simulation-based learning has become an important part of residents' training and is already well established in fields such as anesthesia and emergency medicine. In the domain of OTL-HNS, it is gradually gaining in popularity. Crisis Resource Management (CRM), a program adapted from the aviation industry, aims to improve outcomes of crisis situations by attempting to mitigate human errors. Some examples of CRM principles include cultivating situational awareness; promoting proper use of available resources; and improving rapid decision making, particularly in high-acuity, low-frequency clinical situations. Our pilot project sought to integrate CRM principles into an airway simulation course for OTL-HNS residents, but most important, it evaluated whether learning objectives were met, through use of a novel error identification model.
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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 | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".