Three-Dimensional Vascular Anatomical Study of the Tensor Fasciae Latae Muscle and Perforators
Bibliographic record
Abstract
Background To harvest any flap on the lateral circumflex femoral artery (LCFA) including tensor fasciae latae (TFL) muscle, a precise description of the vascular anatomy is required. There have been conflicting reports of the vascular supply of TFL and its overlying skin. The objective of this study was to evaluate the anatomy of the TFL muscle according to the location, origin, type, caliber, and length of vessels that supply the muscle. Methods This study was performed on human cadavers (n = 16 thighs) that were injected with a mixture of lead oxide and gelatin through the femoral artery. Whole body computed tomography scans were performed. Three-dimensional images of the arterial anatomy were created using Materialise Interactive Medical Image Control Software (MIMICS). Anatomical dissection of all cadaver thighs was performed to visualize the arterial blood supply of the muscle and its regional perforators. Results Sixteen thighs were included in the study. The main arterial supply of the TFL muscle was in all cases, the ascending branch of the LCFA (LCFA-asc) artery. The mean external diameter of the LCFA-asc artery was 2.7 mm ± 0.4 and the mean length was 3.6 cm ± 0.6. The distance from the anterior superior iliac spine to point where the vascular pedicle reaches the muscle ranged from 6.7 to 10.2 cm. The average number of cutaneous perforators was 10.9 ± 4. There were musculocutaneous perforators in all of our dissections (n = 16) and 14 of our specimens had septocutaneous perforators. Conclusion The main vascular supply to the TFL muscle is the ascending branch of the LCFA, which also gives rise to septocutaneous and musculocutaneous perforators. MIMICS provides excellent three-dimensional anatomical information about the vascular supply of the TFL.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 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".