Groupe pour L’Avancement de la Microchirurgie Canada (GAM)
Bibliographic record
Abstract
PURPOSE: To maximize the benefits of deep inferior epigastric perforator flaps (DIEP) flaps, they should be based on one or two perforators.Harvesting large volumes of tissue on a limited number of perforators occasionally results in flaps with inadequate venous drainage.We routinely use the superficial inferior epigastric vein (SIEV) as a "bail out" for this situation.METHODS: 200 consecutive DIEP flaps were reviewed.RESULTS: 10 of the 200 DIEP flaps (5%) demonstrated clinical venous insufficiency, despite patency of the deep inferior epigastric veins.9 of the 10 were "hemi flaps" (zones I & III).One flap included all 4 zones.8 of the 10 were deemed congested during the initial surgery and an SIEV bailout was immediately performed.2 were thought only to be "slightly congested" but ultimately required an SIEV bailout when taken back at a later time.The SIEV anastomosis were performed to the following recipients: IMV in 5 patients; retrograde into the distal stump of an IMV in 1; a local unnamed lateral chest wall vein in 2; to a branch of the thoracoacromial vein in 1; and the thoracodorsal vein using a vein graft in 1.There was no total or partial flap necrosis.CONCLUSIONS: The dominant venous drainage pattern of the lower abdominal skin and fat is through the superficial venous system (SIEV).DIEP flaps may have problems with venous drainage due to inadequate collaterals or the presence of valves between the deep and superficial venous systems.Utilizing the SIEV can salvage a DIEP flap when this situation occurs.We present a structured algorithm for managing this situation.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.035 | 0.004 |
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".