Simplified Summative Temporal Bone Dissection Scale Demonstrates Equivalence to Existing Measures
Bibliographic record
Abstract
INTRODUCTION: Emphasis on patient safety has created the need for quality assessment of fundamental surgical skills. Existing temporal bone rating scales are laborious, subject to evaluator fatigue, and contain inconsistencies when conferring points. To address these deficiencies, a novel binary assessment tool was designed and validated against a well-established rating scale. METHODS: Residents completed a mastoidectomy with posterior tympanotomy on identical 3D-printed temporal bone models. Four neurotologists evaluated each specimen using a validated scale (Welling) and a newly developed "CanadaWest" scale, with scoring repeated after a 4-week interval. RESULTS: Nineteen participants were clustered into junior, intermediate, and senior cohorts. An ANOVA found significant differences between performance of the junior-intermediate and junior-senior cohorts for both Welling and CanadaWest scales ( P < .05). Neither scale found a significant difference between intermediate-senior resident performance ( P > .05). Cohen's kappa found strong intrarater reliability (0.711) with a high degree of interrater reliability of (0.858) for the CanadaWest scale, similar to scores on the Welling scale of (0.713) and (0.917), respectively. CONCLUSION: The CanadaWest scale was facile and delineated performance by experience level with strong intrarater reliability. Comparable to the validated Welling Scale, it distinguished junior from senior trainees but was challenged in differentiating intermediate and senior trainee performance.
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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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".