The Development of a Benchtop Breast Reconstruction Surgical Simulator
Bibliographic record
Abstract
A modified Delphi technique was used to survey plastics surgeons with an expertise in breast reconstruction from 6 university centers with plastic surgery residency programs across Canada. A list of the most challenging steps in teaching alloplastic breast reconstruction was obtained. From the survey results, a benchtop post-mastectomy breast reconstruction simulator was created using various silicon materials. The simulator was designed to be completely reusable with no disposable components necessary for each use. Senior plastic surgeons (n= 6) with an expertise in breast reconstruction were recruited and asked to perform a sub-pectoral, implant-based breast reconstruction on the simulator. 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.Preliminary face and content validation results based on the evaluations performed by those expert plastic surgeons showed excellent results among parameters evaluated, with an overall mean score of 4.7 on 5 (94.0%). Evaluators considered the six relevant anatomical components that were successfully included in the simulator.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".