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Use of a Hard Palate Mucoperiosteal Flap for Rostral Muzzle Reconstruction in a Dog after a Traumatic Premaxillary Degloving Injury

2012· article· en· W1532322605 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueVeterinary Surgery · 2012
Typearticle
Languageen
FieldMedicine
TopicReconstructive Facial Surgery Techniques
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsMedicineDeglovingNostrilAmputationMuzzleSurgeryHard palateReconstructive surgeryAnatomyDentistryNose

Abstract

fetched live from OpenAlex

OBJECTIVE: To describe a technique for reconstruction of the rostral aspect of the muzzle of a dog after traumatic amputation. STUDY DESIGN: Clinical report. ANIMALS: Adult female dog. METHODS: A 6-year-old, intact, female, mixed-breed dog was admitted for facial reconstructive surgery after traumatic amputation of the rostral aspect of the muzzle. The nasal planum and the rostral portion of the upper lips were missing. A hard palate mucoperiosteal flap and lateral labial advancement flaps were used to reconstruct the nasal philtrum and borders of the nares. RESULTS: This reconstructive technique resulted in adequate nostril function and an acceptable cosmetic outcome. One naris developed partial obstruction with granulation tissue that may have occurred because of a lack of circumferential nasal mucosa to appose the skin on that side. CONCLUSION: The mucoperiosteum of the hard palate can be used to reconstruct the rostral aspect of the muzzle after traumatic amputation, resulting in an acceptable cosmetic outcome.

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.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.097
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.105
GPT teacher head0.319
Teacher spread0.214 · 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