Development of a tool for Global Rating of Endoscopic Surgical Skills (GRESS) for assessment of otolaryngology residents.
Bibliographic record
Abstract
OBJECTIVE: To develop a valid and reliable assessment tool for endoscopic sinus surgery (ESS). MATERIAL AND METHODS: Data were collected prospectively in an observational study through evaluations at two tertiary academic institutions, i.e. St. Paul's Sinus Centre, St. Paul's Hospital, Vancouver, British Columbia, Canada, and King Fahd Medical City, Riyadh, Saudi Arabia, from December 2006 to December 2009. A 2-page evaluation form was developed in conjunction with the Objective Assessment of Technical Skills Surgery (OSATS) evaluation form developed by Reznick et al in Toronto to assess residents' surgical skills. A Likert scale (1-5 where 5 = excellent) was used for evaluations. The Global Rating of Endoscopic Surgical Skills (GRESS) evaluation instrument was designed with input from academic otolaryngologists, fellowship-trained rhinologists, and experts in medical education. The experts' comments were incorporated, establishing face and content validity. Residents from various levels of training were assessed objectively using this instrument. Internal consistency was evaluated using Cronbach's alpha. Test-retest and inter-rater reliability was measured using intra-class correlation. RESULTS: A total of 31 assessments were completed by 15 residents. GRESS showed high reliability in the context of internal consistency (alpha = 0.99), test-retest (0.95, CI = 0.83-0.98), and inter-rater reliability (0.86, CI = 0.31-0.98). CONCLUSIONS: This pilot study demonstrated that GRESS is a valid and reliable assessment tool for operating room 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 | 0.017 | 0.041 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".