Creating a cross‐institutional grading scale for temporal bone dissection
Bibliographic record
Abstract
OBJECTIVES/HYPOTHESIS: There is increasing interest in objective assessment of surgeon competence. In the field of otolaryngology, several surgical training programs, including The Ohio State University, the University of Toronto, and Stanford University, have pursued standardized criteria to rate their trainees' performance in the initial steps of temporal bone dissection (complete mastoidectomy with facial recess approach). Although these assessment metrics require the completion of similar basic components integral to successful temporal bone dissection, certain listed criteria are unique to each institution. Our aim was to establish a more standardized set of criteria that can be used across different institutions to objectively assess temporal bone dissection. We translated these new criteria into automated metrics in our temporal bone dissection simulator to achieve even more objective grading of temporal bone dissections. STUDY DESIGN: Cross-sectional study/survey. METHODS: The temporal bone assessment criteria developed by each of the three aforementioned institutions were compiled into an all-encompassing scale. This compilation was sent out as an online survey to members of the American Neurotology Society and American Otological Society with instructions to rate the importance of each criterion. RESULTS: Criteria that were ranked by >70% of respondents as either "very important" or "important" were used to create the new, cross-institutional scale for the objective assessment of temporal bone dissection. CONCLUSIONS: The new assessment scale and its eventual incorporation into the temporal bone surgical simulator will enhance the objectivity of currently existing methods to evaluate surgical performance across different institutions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".