Computed tomography-based preoperative vascular imaging in autologous breast reconstruction: A Canadian perspective
Bibliographic record
Abstract
There appears to be increased use of computed tomography angiography (CTA) in the preoperative planning of autologous perforator flap breast reconstruction. Despite the advantages of providing superior anatomical detail, concerns regarding cost and radiation exposure of this technique remain. In the current study, a paper-based survey was distributed to 44 plastic surgeons with a special interest in breast reconstruction at 19 different centres across Canada to collect their perspectives and practice characteristics with respect to the use of CTA as a preoperative imaging modality in breast reconstruction. The response rate of the survey was 75%. The majority of respondents commonly use perforator flap breast reconstruction and CTA in their breast reconstruction practice. Surgeons identified particular benefits of CTA in patients who had previously undergone abdominal surgery. However, more than one-half of the overall cohort was concerned about radiation exposure associated with CTA. A review of the literature suggests that it may be worthwhile to reduce the unnecessary risks of additional radiation exposure to the breast cancer population. A prospective study may help to better define the group of patients in whom CTA will provide optimal benefits in terms of reducing perioperative microvascular morbidity.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".