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Record W2999800848 · doi:10.1111/vsu.13375

Diagnostic needle arthroscopy of the tarsocrural joint in standing sedated horses

2020· article· en· W2999800848 on OpenAlexaff
Dimitri T. N. Kadic, Alvaro G. Bonilla

Bibliographic record

VenueVeterinary Surgery · 2020
Typearticle
Languageen
FieldVeterinary
TopicVeterinary Equine Medical Research
Canadian institutionsUniversité de MontréalCegep de Saint Hyacinthe
Fundersnot available
KeywordsMedicineArthroscopySurgery

Abstract

fetched live from OpenAlex

OBJECTIVE: To develop and assess a needle arthroscopic technique to diagnose conditions of the tarsocrural joint (TCj) in standing sedated horses. STUDY DESIGN: Experimental study. SAMPLE POPULATION: Six cadaveric hind limbs (phase 1) and six healthy horses (Phase 2). METHODS: In phase 1, each TCj was examined with a 1.2-mm-needle arthroscope. Suitability of the needle arthroscope and degree of joint visualization with traditional arthroscopic approaches were assessed. In phase 2, the feasibility of the procedure was assessed in six standing healthy horses. A custom-made splint and base were developed to maintain joint flexion during the procedure. RESULTS: Thorough evaluation of the dorsal intra-articular structures of the TCj via dorsomedial and dorsolateral approaches was possible in both phases. The procedure was feasible, quickly performed, and well tolerated by all horses. Complications consisted of moderate movement (2/6 horses) and hemarthrosis (3/6 horses). CONCLUSION: Diagnostic standing needle arthroscopy of the TCj allowed thorough evaluation of the dorsal aspect of the joint while avoiding the cost and risks associated with general anesthesia. Inadvertent puncture of the dorsomedial vasculature with the cannula and obturator led to significant hemarthrosis. CLINICAL IMPACT: Needle arthroscopy of the TCj offers an alternative diagnostic tool when traditional imaging techniques (radiography and ultrasonography) are unrewarding or nondiagnostic. The technique is conceived mainly for diagnostic purposes, but its use during short interventions warrants investigation.

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)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.766
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.263
GPT teacher head0.364
Teacher spread0.101 · 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 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

Citations14
Published2020
Admission routes1
Has abstractyes

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