Comparison of Summative Temporal Bone Dissection Scales Demonstrate Equivalence
Bibliographic record
Abstract
Abstract Introduction Temporal bone surgery is a unique and complicated surgical skill that requires extensive training. There is an educational requirement to maximize trainee experience and provide effective feedback. Objective We evaluate three temporal bone dissection scales for efficacy, reliability, and accuracy in identifying resident skill during temporal bone surgery. Methods Residents of various skill levels performed a mastoidectomy with posterior tympanotomy on identic 3D-printed temporal bone models. Four blinded otologic surgeons evaluated each specimen at two separate intervals using three separate dissection scales: the Welling Scale (WS), the Iowa Temporal Bone Assessment Tool (ITBAT), and the CanadaWest Scale (CWS). Scores from each scale were compared in their ability to accurately separate residents by skill level, inter- and intrarater reliability, and efficiency in application. Results Nineteen residents from 9 postgraduate programs participated. Assessment was clustered into junior (postgraduate year or PGY 1, 2), intermediate (PGY 3) and senior resident (PGY 4, 5) cohorts. Analysis of variance (ANOVA) found significant differences between cohort performance (p < 0.05) for all 3 scales considering the PGY level and the subjective account of temporal bone surgical experience. The inter-rater reliability was consistent across each scale. The intrarater reliability was comparable between the CWS (0.711) and the WS (0.713), but not the ITBAT (0.289). Time (in seconds) to complete scoring for each scale was also comparable between the CWS (42.7 ± 16.8), the WS (76.6 ± 14.5), and the ITBAT (105.6 ± 38.9). Conclusion All three scales demonstrated construct validity and consistency in performance, and consideration should be given to judicious use in training.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.019 | 0.097 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".