Bibliographic record
Abstract
Breast reconstruction following mastectomy remains an essential component of the holistic approach to treating women affected by breast cancer. The training of plastic surgery residents in this domain can prove to be challenging due to limited access to non-patient models. Advanced and increased simulation-based training is one way to teach residents necessary skills, improve outcomes of surgery and create a dynamic teaching environment. A modified Delphi technique was used to survey plastics surgeons with an expertise in breast reconstruction from six university centers with plastic surgery residency programs across Canada. From the survey results, a list of the most challenging steps in teaching alloplastic breast reconstruction was obtained. A benchtop post-mastectomy breast reconstruction simulator was created using various silicone materials. The simulator was designed to be completely reusable with no disposable components necessary for each use. Senior plastic surgeons (n= 9) with an expertise in breast reconstruction and plastic surgery residents (n=9) were recruited and asked to perform a sub-pectoral, implant-based breast reconstruction on the simulator. Participants' performance was recorded in a blinded fashion with all identifying information removed. Following the procedure, participants were asked to complete a survey and grade the simulator on its physical attributes, realism of experience, realism of material and overall experience. The blinded videos of the participants' performances were graded by two independent reviewers. Three evaluation metrics were used consisting of a global rating scale, checklist score and surgery specific scale. Preliminary face and content validation results based on the evaluations performed by the expert plastic surgeons showed excellent results among parameters evaluated, with an overall mean score of 4.7 on 5 (94.0%). When performance between staff surgeons and residents were compared, a statistically significant difference was found on the global rating scale (36.4 (2.1) vs. 23.9 (4.0)) and surgery specific scale (39.9 (1.8) vs. 28.6 (3.6)), p<0.0001. Given the realism offered by the simulator as well as its reusability and proven validity, we believe that this simulator has the potential to significantly improve the way in which breast reconstruction is taught and mastered by plastic surgery trainees with the goal to improve patient outcomes and maximize patient safety.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".