The Effectiveness of Video Technology As an Adjunct to Teach and Evaluate Epidural Anesthesia Performance Skills
Bibliographic record
Abstract
BACKGROUND: Although video review has been used in teaching, it has not been reported for use as an adjunct to teaching anesthesiology residents. The purpose of the prospective, randomized, blinded study was to determine whether teaching with video review improves epidural anesthesia skills of anesthesiology residents. METHODS: Twenty-two second-year (CA-2) anesthesiology residents beginning their first obstetric anesthesia rotation were assigned to video or non-video groups. All residents were filmed daily as they placed epidural analgesia. Residents assigned to the video group reviewed their tapes twice a week with an attending anesthesiologist, whereas residents assigned to the non-video group never saw their films. Four experienced attending anesthesiologists independently judged videotapes taken on days 1, 15, and 30 and scored the residents for "overall" skill (range of summed overall grades, 0-40), as well as on 13 predetermined criteria. RESULTS: As determined by kappa coefficients, interrater reliability was high among the judges (k = 0.7-0.8). Residents in the video group improved to a greater degree than residents in the non-video group. On day 1, the median overall grades for the video and non-video groups were 21 and 12, respectively. By day 15, the corresponding grades had increased to 32 and 24, respectively (P < 0.01). However, overall median grades continued to improve between days 15 and 30 in the video group only (P < 0.01). CONCLUSIONS: Review of resident videotapes resulted in greater improvement in overall and predetermined performance criteria. In addition, video review was helpful in identifying skills that were inadequately learned, thus allowing for specific teaching in those areas.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".