Preoperative Perforator Mapping in SGAP Flap: Does Magnetic Resonance Imaging Make the Difference?
Bibliographic record
Abstract
BACKGROUND: Preoperative vascular mapping has emerged as an excellent adjunct to perforator flap surgery, improving operative time while aiding the surgeon in the selection of the ideal perforator. This study evaluated the effect of preoperative vascular mapping by magnetic resonance imaging to identify tissue for a superior gluteal artery perforator (SGAP) flap on total operative time and compared radiologic mapping by magnetic resonance imaging with Doppler ultrasonography for perforator localization. The authors also investigated whether drawing the flap according to magnetic resonance imaging perforator localization or tissue availability affected the outcome of the donor site. METHODS: A prospective study was performed on patients undergoing SGAP flap breast reconstruction. Patients were randomized into two groups. One group received preoperative magnetic resonance imaging for flap tissue planning based on localization of the perforator. The other group received flap planning based on tissue availability and then underwent external Doppler ultrasonography to identify the dominant perforator. An ad hoc outcome scale was created to evaluate outcomes of the donor sites. RESULTS: Preoperative vascular mapping by magnetic resonance imaging or external Doppler ultrasonography was performed the day of surgery on 35 and 27 patients, respectively. The mean flap elevation times of the imaging patients versus the ultrasonography patients were 252 and 228 minutes, respectively. The differences between flap elevation times and cosmetic outcomes for the two patient groups were not significant. CONCLUSION: The authors' findings indicate that the use of magnetic resonance imaging for SGAP flap planning did not reduce operative time, and that donor-site outcomes were not affected by the modality used for preoperative perforator mapping. CLINICAL QUESTION/LEVEL OF EVIDENCE: Therapeutic, II.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".