Université de Montréal Objective and Structured Checklist for Assessment of Audiovisual Recordings of Surgeries/techniques (UM-OSCAARS): a validation study
Bibliographic record
Abstract
Background: Use of videos of surgical and medical techniques for educational purposes has grown over the last years. To our knowledge, there is no validated tool to specifically assess the quality of these types of videos. Our goal was to create an evaluation tool and study its intrarater and interrater reliability and its acceptability. We named our tool UM-OSCAARS (Université de Montréal Objective and Structured Checklist for Assessment of Audiovisual Recordings of Surgeries/techniques). Methods: UM-OSCAARS is a grid containing 10 criteria, each of which is graded on an ordinal Likert-type scale of 1 to 5 points. We tested the grid with the help of 4 voluntary otolaryngology - head and neck surgery specialists who individually viewed 10 preselected videos. The evaluators graded each criterion for each video. To evaluate intrarater reliability, the evaluation took place in 2 different phases separated by 4 weeks. Interrater reliability was assessed by comparing the 4 topranked videos of each evaluator. Results: There was almost-perfect agreement among the evaluators regarding the 4 videos that received the highest scores from the evaluators, demonstrating that the tool has excellent interrater reliability. There was excellent test-retest correlation, demonstrating the tool's intrarater reliability. Conclusion: The UM-OSCAARS has proven to be reliable and acceptable to use, but its validity needs to be more thoroughly assessed. We hope this tool will lead to an improvement in the quality of technical videos used for educational purposes.
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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 | 0.014 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".