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Record W2064991580 · doi:10.3415/vcot-14-04-0056

Pedicle digital pad transfer and negative pressure wound therapy for reconstruction of the weight-bearing surface after complete digital loss in a dog

2014· article· en· W2064991580 on OpenAlexaboutno aff
B. Van Goethem, Anneleen L. Spillebeen, P Vandekerckhove, J. Saunders, H. de Rooster, Matan Or

Bibliographic record

VenueVeterinary and Comparative Orthopaedics and Traumatology · 2014
Typearticle
Languageen
FieldMedicine
TopicReconstructive Surgery and Microvascular Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineSurgeryBandageGranulation tissueAmputationNegative-pressure wound therapyLabrador RetrieverWound healing

Abstract

fetched live from OpenAlex

A young Labrador Retriever was presented for treatment of severe distal hindlimb necrosis caused by bandage ischemia. During digit amputation at the metatarsophalangeal joints, the third and fourth digital pads were salvaged and transferred to the metatarsal stump to create a weight-bearing surface. Negative pressure wound therapy (NPWT) was utilized for flap immobilization and to promote granulation tissue in the remaining wound defect. Sturdy adherence of the digital pads was achieved after only four days. The skin defect healed completely by second intention and the stump was epithelialized with a thin pad after three months. At the nine month follow-up examination, the stump had a thick hyperkeratinized pad. The dog walked and ran without any apparent signs of discomfort and compensated for the loss of limb length by extending the stifle and tarsocrural joints. Despite a challenging wound in a difficult anatomical location, digital pad flap transfer and NPWT proved successful in restoring long-term ambulation in an active large breed dog.

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 score0.051
Threshold uncertainty score0.491

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.033
GPT teacher head0.267
Teacher spread0.234 · 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

Citations8
Published2014
Admission routes1
Has abstractyes

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