Objective Assessment of Temporal Bone Drilling Skills
Bibliographic record
Abstract
OBJECTIVES: There is great interest in training surgeons in the technical aspects of their craft through simulation and laboratory-based exercises. However, there are as yet only a few objective tools to assess technical performance in a laboratory setting. This study assesses three potential objective assessment tools for a traditional otolaryngology laboratory exercise, temporal bone drilling. METHODS: We performed a validation study in an academic training program. Nineteen otolaryngology residents performed a cortical mastoidectomy on a cadaveric temporal bone. The participants were divided into two groups, experienced and novice, based on previous temporal bone drilling experience. Performance was rated by two independent, blinded experts using four different assessments, the Global Rating Scale (GRS), the Task-Based Checklist (TBC), the final product analysis (FPA), and expert opinion (EO). RESULTS: The interrater reliability for all four assessments was good. Two potential objective assessments, the GRS and the TBC, and the traditional assessment tool of EO, correlated with trainee experience. The FPA, however, did not correlate with trainee experience. A logistic regression analysis of all assessments showed that the TBC correlates with EO. CONCLUSIONS: This study validates EO, the GRS, and the TBC as measures of temporal bone drilling performance. Of these measures, the TBC correlates best with EO according to logistic regression and can be reliably used as an objective assessment of temporal bone drilling.
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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".