Bibliographic record
Abstract
Thank you for your comments regarding our study of video feedback as a means of enhancing surgical training. We are certainly in agreement with both you and the literature, which has clearly demonstrated the beneficial effect of bench model training in the development of surgical technical skills.1,2,3,4,5,6 Some of these investigations have provided evidence of transfer to the human model.7 In fact, much of this work has been conducted right here at our centre. The design of our study provided ample opportunity for practice before application of the intervention to the experimental group. During 3 laboratory sessions, residents practised the surgical task and received extensive individual feedback from vascular surgeons. Residents were free to ask questions of the experts, and the experts were free to provide verbal feedback as they circulated through the work stations. It is true that this study did not have a stepwise progression in model fidelity similar to the described subfascial endoscopic perforator surgery, but this was not the purpose of our study. Our aim was to look for any improvement among groups that was attributable to video feedback. We were not attempting to develop the best possible bench model strategy. Our findings corroborated earlier work, which also found no significant benefits of videotaped feedback among orthopedic surgical residents using technical skills of varying difficulty.8 We believe that there is either no benefit attributable to video feedback or we do not possess measurement tools sensitive enough to recognize them. It is our opinion that a more extensive bench model training strategy such as in SEPS is unlikely to provide clear evidence that video feedback is beneficial. David Backstein, MD, MEd Division of Orthopaedic Surgery Mount Sinai Hospital Toronto, Ont.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".