A cadaveric study and clinical application of the free superficial circumflex iliac artery perforator flap reconstruction of limb soft tissue defects
Bibliographic record
Abstract
Introduction In recent decades the superficial circumflex iliac artery perforator (SCIP) flap technique has gradually overcome the previous disadvantages of its pedicle and donor site to be widely applied in treating tissue's defects. This article investigates the vascular anatomy of the SCIP flap and evaluates the result of using the flap to reconstruct limb defects. Materials and Methods The study was performed in two parts. Firstly a cross-sectional descriptive anatomical study comprising 49 groin dissections in 25 cadavers, with contrast radiographic demonstration of the vascular anatomy. Secondly a retrospective case series of 51 free SCIP flaps in 49 patients. Surgical outcomes were evaluated using the Vancouver Scar Scale on the regional function and the aesthetics of implanted flap and donor sites. Results The results of anatomical analysis on cadavers showed the superficial circumflex iliac arteries were detected in all specimens with an average diameter of 1.4 ± 0.3 mm. The superficial subcutaneous venous systems were also recorded, including two veins (superficial circumflex vein and superficial epigastric vein) in 79.5% with an average diameter of 2.3 ± 0.6 mm. The flap results showed the alive flap rate was 90.2%, partial-necrosis flap was 3.9% and total necrosis of the flap in 5.9%. In the aesthetic evaluation, fine line scars were seen in 13/51 cases (25.5%), stretch scars in 33/51 cases (64.7%) and hypertrophic scars in 5/51 patients (9.8%), with no cases of keloid scars. Conclusion The SCIP flap is a reliable free-tissue transfer for reconstructing limb soft tissue defects. Moreover, the SCIP flap is an ideal flap with pleasing aesthetics because the donor-site scar is situated in a well-hidden site. Care must be taken when vein grafts are used to lengthen the flap as flap necrosis rates increase.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".