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Record W2989844685 · doi:10.1111/vsu.13352

Sinocutaneous fistula repair with a masseter muscle transposition flap combined with wound matrix and cancellous bone graft in a horse: A new technique

2019· article· en· W2989844685 on OpenAlexaff
Seiji Yoshimura, Spencer Μ. Barber, Michelle L. Tucker, José L. Bracamonte, Suzanne J. K. Mund, Keri L. Thomas

Bibliographic record

VenueVeterinary Surgery · 2019
Typearticle
Languageen
FieldVeterinary
TopicVeterinary Equine Medical Research
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsMedicineMasseter muscleSurgeryFistulaAnatomy

Abstract

fetched live from OpenAlex

OBJECTIVE: To describe a new technique to repair a sinocutaneous fistula with a masseter muscle transposition flap. STUDY DESIGN: Case report. ANIMAL: One 13-year-old thoroughbred stallion. METHODS: One 13-year-old stallion with a 3.5 × 6-cm sinocutaneous fistula over the right caudal maxillary sinus was treated with a transpositional masseter muscle flap. This repair consisted of a commercial wound matrix dressing placed directly over the hole in the maxilla and secured with suture material; a cancellous bone graft collected from the right tuber coxa placed on the dressing; and a portion of the superficial layer of the masseter muscle, with its pedicle at the facial crest, transposed dorsally over the bone graft, followed by a rotational skin flap with skin rostral to the fistula to close the defect. RESULTS: Seroma formation and dehiscence of the skin flap occurred, but the transposed muscle flap survived, and the technique resulted in successful closure of the sinocutaneous fistula with excellent cosmetic and functional outcome. CONCLUSION: A chronic maxillary sinocutaneous fistula was successfully treated by using a transposition flap of the masseter muscle and a rotational skin flap with minor complications. CLINICAL IMPACT: Transposition of the superficial layer of the masseter muscle should be considered for a repair of large maxillary sinocutaneous fistulas in horses.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.549
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.032
GPT teacher head0.300
Teacher spread0.269 · 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.

Study designCase report
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

Citations5
Published2019
Admission routes1
Has abstractyes

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