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Record W4323037280 · doi:10.2460/ajvr.22.11.0189

A biomechanical comparison of a novel two-loop suture technique and two sutures for laryngoplasty in the horse

2023· article· en· W4323037280 on OpenAlexaff
David G. Wilson, Imma Roquet, Michelle L. Tucker, James L. Carmalt

Bibliographic record

VenueAmerican Journal of Veterinary Research · 2023
Typearticle
Languageen
FieldVeterinary
TopicVeterinary Equine Medical Research
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsLaryngoplastyHorseFibrous jointLoop (graph theory)MedicineAnatomySurgeryBiologyMathematicsLarynx

Abstract

fetched live from OpenAlex

OBJECTIVES: Evaluation of the strength of the novel suture technique by comparison with a 2-interrupted suture technique. SAMPLE: 40 equine larynges. PROCEDURES: 40 larynges were used; 16 laryngoplasties were performed using the currently accepted 2-suture technique and 16 using the novel suture technique. These specimens were subjected to a single cycle to failure. Eight specimens were used to compare the rima glottidis area achieved with 2 different techniques. RESULTS: The mean force to failure, as well as the rima glottidis area of both constructs, were not significantly different. The cricoid width did not have a significant effect on the force to failure. CLINICAL RELEVANCE: Our results suggest that both constructs are equally strong and can achieve a similar cross-sectional area of the rima glottidis. Laryngoplasty ("tie-back") is currently the treatment of choice for horses with exercise intolerance due to recurrent laryngeal neuropathy. Failure to maintain the expected degree of arytenoid abduction post-surgery occurs in some horses. We believe this novel 2-loop pulley load-sharing suture technique can help achieve and, more importantly, maintain the desired degree of abduction during surgery.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.000
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 head0.342
GPT teacher head0.560
Teacher spread0.218 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations1
Published2023
Admission routes1
Has abstractyes

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