A prototype 3D modelling and visualisation pipeline for improved decision-making in breast reconstruction surgery
Why this work is in the frame
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Bibliographic record
Abstract
In breast reconstruction after a single mastectomy, the surgeon must choose from hundreds of implants to select the one that best replicates the patient’s natural breast. Due to a lack of measurement tools, the surgeon must depend on their previous experience to visually choose the best implant, leading them to compare and use numerous implants to confirm the implant of choice for each patient. In this paper, we investigate the use of finite element modelling (FEM) for improving pre-operative decision-making in determining the optimal implant for a patient based on pre-operative MRI scans. The findings of our preliminary investigation show that FEM can be used to provide input for a comparison system, which can rank implants based on their similarity to a patient model of the natural breast, and the system’s choices are comparable to what human users would make for each patient.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 it