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Record W2480387737

Fine-needle aspiration in the diagnosis of equine skin disease and the epidemiology of equine skin cytology submissions in a western Canadian diagnostic laboratory.

2016· article· en· W2480387737 on OpenAlexaffabout
Erin Zachar, Hilary J Burgess, Bruce Wobeser

Bibliographic record

VenuePubMed · 2016
Typearticle
Languageen
FieldVeterinary
TopicInfectious Diseases and Mycology
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsMedicineCytologyFine-needle aspirationGynecologySurgeryPathologyBiopsy
DOInot available

Abstract

fetched live from OpenAlex

Fine-needle aspiration (FNA) is commonly used to diagnose skin disease in companion animals, but its use in horses appears to be infrequent. Equine veterinarians in western Canada were surveyed to determine their opinions about FNA and 15 years of diagnostic submissions were used to compare the perceived to actual value of FNA in the diagnosis of skin disease in horses. Practitioners viewed FNA as quick, easy, economical, and minimally invasive. However, most veterinarians rarely chose to use FNA due to a perception that sample quality and diagnostic yield were poor and there was a narrow range of diseases the technique could diagnose. Analysis of the FNA cytology samples from a veterinary diagnostic laboratory showed a wide variety of equine skin disease conditions, but the frequency of non-diagnostic results was significantly higher in equine submissions compared to those from dogs and cats.

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)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.178
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.048
GPT teacher head0.298
Teacher spread0.250 · 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

Citations3
Published2016
Admission routes2
Has abstractyes

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