Digital Vascular Mapping of the Integument About the Achilles Tendon
Bibliographic record
Abstract
BACKGROUND: Soft-tissue coverage and vascularity likely play a vital role in the genesis of wound complications and infections during open Achilles tendon repair. Planning an appropriate surgical approach might decrease the prevalence of these complications. METHODS: Five adult cadavers underwent whole-body arterial perfusion with a mixture of lead oxide, gelatin, and water. The skin of the foot and ankle was dissected, and the vascular supply was evaluated with angiography. All angiograms were analyzed with use of statistical software. RESULTS: We constantly identified three vascular zones: (1) the medial vascular zone, which had the richest blood supply; (2) the lateral vascular zone, in which the density of vascularity was good and much better than that in the posterior zone; and (3) the posterior vascular zone, which showed the poorest blood supply. CONCLUSIONS: The richest vascular zones of the skin covering the Achilles tendon are located toward the medial and lateral aspects of the Achilles tendon. On the basis of the present study, we recommend using a medial or lateral incision in the integument covering the tendon, as the posterior incision will be located in a less vascular zone. CLINICAL RELEVANCE: The present study should help the surgeon to plan the surgical approach to the Achilles tendon by designing skin incisions in a more vascular zone.
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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 | 0.000 | 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) | 0.005 | 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".