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Record W2516210041

Feline onychectomy: Current practices and perceptions of veterinarians in Ontario, Canada.

2016· article· en· W2516210041 on OpenAlexaffabout
Lori R. Kogan, Susan E. Little, Peter W. Hellyer, Regina Schoenfeld‐Tacher, Rebecca Ruch-Gallie

Bibliographic record

VenuePubMed · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHuman-Animal Interaction Studies
Canadian institutionsBytown Cat Hospital
Fundersnot available
KeywordsHumanitiesFamily medicineMedicinePolitical sciencePsychologyArt
DOInot available

Abstract

fetched live from OpenAlex

The objective of the study was to determine the proportion of practitioners from Ontario, Canada who perform onychectomy, identify the techniques utilized, and obtain practitioners views on the procedure. An anonymous survey was distributed to Ontario Veterinary Medical Association members. Mann-Whitney U-tests were used to compare responses of opinion questions related to declawing between respondents who indicated they perform declawing procedures and those who do not. Of 500 respondents, 75.8% reported performing onychectomy, with 60.1% of those reporting performing the procedure less than monthly and 73.3% only performing the procedure after recommending alternatives. Statistically significant differences were found between those who do and those who do not perform onychectomy for perception of procedural pain, concept of mutilation, perception of procedural necessity for behavior modification or prevention of euthanasia, and support of province-wide procedural bans.

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 score0.078
Threshold uncertainty score0.157

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.037
GPT teacher head0.322
Teacher spread0.285 · 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

Citations5
Published2016
Admission routes2
Has abstractyes

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