Excellent Agreement of In-Person Scoring versus Scoring of Digitally Recorded Simulated Suture Skills Examination
Bibliographic record
Abstract
Abstract This study’s objective was to evaluate the agreement between in-person performance scores and digitally recorded assessment scores by the same examiner using a simulated suturing skill examination. With ethics approval, veterinary students underwent a simulated skills examination proctored by an in-person examiner and simultaneously digitally recorded using two wide-angle cameras mounted overtop and to the side of the surgical field. Performance scores were based on a nine-item rubric. In-person examination scores were compared for agreement with those obtained by blind review of the digital recording of the same session, by the same examiner, 6–18 months following the in-person examination. Thirty-nine students were enrolled. All rubric categories could be assessed during digital assessment of the recording from the camera mounted above the surgical area. In two instances, the side digital recording had to be reviewed to confirm correct needle holder grip. Concordance correlation between performance scores from in-person and post hoc digital assessment was excellent ( r = .93). The excellent agreement between in-person and digital assessment suggests that digitally recording skills examinations can provide adequate suturing skills assessment, presenting several benefits. Digitally recorded assessment allows for anonymity, which can reduce assessor bias/favoritism, provide a record of performance that students can review and critically self-reflect upon, and reduce the number of in-person examiners required to complete surgical skills examinations. Additionally, digitally recorded assessment could become an option for students who experience anxiety performing a skills exam in the presence of an examiner.
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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 | 0.020 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".