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Record W2415715620

Evaluation of 21 426 feline bladder urolith submissions to the Canadian Veterinary Urolith Centre (1998-2014).

2016· article· en· W2415715620 on OpenAlexaffabout
Houston Dm, Vanstone Np, Moore Ae, Weese He, Weese Js

Bibliographic record

VenuePubMed · 2016
Typearticle
Languageen
FieldVeterinary
TopicVeterinary Medicine and Surgery
Canadian institutionsRoyal Ottawa Mental Health CentreUniversity of Guelph
Fundersnot available
KeywordsStruviteCalcium oxalateMedicineVeterinary medicineBiologyUrineInternal medicinePhosphate
DOInot available

Abstract

fetched live from OpenAlex

This study reports emerging trends in feline urolithiasis in Canada during the past 16.8 y, evaluates associations of breed and gender with urolith types, and reports on feline submissions from outside of Canada. Struvite and calcium oxalate uroliths comprised > 90% of all uroliths submitted. In cats, oxalate submissions outnumbered struvite submissions from Canada, Hong Kong, Denmark, and the United Arab Emirates, while Australian struvite submissions outnumbered calcium oxalate submissions. In Canada, the majority of urolith submissions were from domestic cats followed by Himalayan, Persian, and Siamese cats. Males were more likely to form calcium oxalate uroliths and females were more likely to develop struvite uroliths. Compared to domestic short-haired cats, Tonkinese, Burmese, Devon rex, Himalayan, Persian, and Siamese cats were significantly associated with calcium oxalate urolith submission. Egyptian mau, Birman, ocicat, and Siamese breeds were over-represented amongst urate submissions.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.102
Threshold uncertainty score0.206

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0050.004
Science and technology studies0.0030.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.147
GPT teacher head0.320
Teacher spread0.172 · 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 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

Citations43
Published2016
Admission routes2
Has abstractyes

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