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Evaluation of Ethyl Alcohol for Use in a Minimally Invasive Technique for Equine Proximal Interphalangeal Joint Arthrodesis

2011· article· en· W1928760640 on OpenAlexaff
Ryan R. E. Wolker, David G. Wilson, Andrew L. Allen, James L. Carmalt

Bibliographic record

VenueVeterinary Surgery · 2011
Typearticle
Languageen
FieldVeterinary
TopicVeterinary Equine Medical Research
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsMedicineArthrodesisInterphalangeal JointJoint (building)SurgeryPathology

Abstract

fetched live from OpenAlex

OBJECTIVE: To determine whether intra-articular 70% ethyl alcohol alone (IAEA) or in combination with 2 percutaneously placed transarticular lag screws (EA-TLS) would result in arthrodesis of the equine proximal interphalangeal (PIP) joint. STUDY DESIGN: Experimental. ANIMALS: Healthy horses (n=6), aged 1.5-3 years, free of lameness, diagonally paired front and hind PIP joints. METHODS: Six milliliters 70% ethyl alcohol was injected into randomly selected diagonally paired front and hind PIP joints. Thirty days later, 2 parallel 5.5 mm cortical screws were inserted in lag fashion across the hind PIP joints and the limbs were cast. Horses were confined for 60 days after surgery before free exercise was permitted. Serial lameness examinations were performed at 1, 6, and 10 months. Radiographs of the PIP joints were obtained before injection with alcohol (front, hind PIP joints), at 6 and 10 months (front PIP joints) and 1, 3, 6, and 10 months (hind PIP joints). At 10 months, horses were euthanatized and gross and histopathologic examination of the treated joints was performed. RESULTS: Horses had variable cartilage thinning (more severe in hind PIP joints) and dorsal bone proliferation. One front and 1 hind PIP joint were fused 10 months after alcohol injection. CONCLUSIONS: Ethyl alcohol injected alone or in combination with percutaneously placed transarticular lag screws failed to reliably produce fusion of the PIP joint.

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)0.009
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.475
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.008
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)0.0010.000

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 head0.555
GPT teacher head0.434
Teacher spread0.121 · 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 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

Citations19
Published2011
Admission routes1
Has abstractyes

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