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Record W2121547448 · doi:10.2106/jbjs.i.00743

Digital Vascular Mapping of the Integument About the Achilles Tendon

2010· article· en· W2121547448 on OpenAlexaff
Horacio Yepes, Maolin Tang, Christopher R. Geddes, Mark Glazebrook, Steven F. Morris, William D. Stanish

Bibliographic record

VenueJournal of Bone and Joint Surgery · 2010
Typearticle
Languageen
FieldMedicine
TopicTendon Structure and Treatment
Canadian institutionsHealth Sciences CentreQueen Elizabeth II Health Sciences CentreDalhousie University
Fundersnot available
KeywordsVascularityMedicineAchilles tendonTendonCadaverAnatomyAnkleSoft tissueBlood supplySurgery

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.376
Threshold uncertainty score0.155

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.015
GPT teacher head0.220
Teacher spread0.205 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations63
Published2010
Admission routes1
Has abstractyes

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