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Record W4387241848

A retrospective study of equine perinatal loss in Canada (2007 to 2020).

2023· article· en· W4387241848 on OpenAlexaffabout
R Madison Ricard, Guillaume St‐Jean, Harveen K Atwal, Bruce Wobeser

Bibliographic record

VenuePubMed · 2023
Typearticle
Languageen
FieldVeterinary
TopicVeterinary Equine Medical Research
Canadian institutionsSaskatchewan Ministry of Agriculture
Fundersnot available
KeywordsMedicineRetrospective cohort studyPediatricsGynecologyInternal medicine
DOInot available

Abstract

fetched live from OpenAlex

Objective: This study aims to identify the most common causes of equine perinatal loss up to 7 d of age in Canada. Animal: Equine. Procedure: Necropsy reports from 360 equine perinatal loss cases were acquired from provincial veterinary diagnostic labs across Canada. Each case was classified into a basic cause (noninfectious, infectious, or unidentified) of perinatal loss, then further classified into primary and secondary categories for analysis. Results: was the most commonly identified bacterial species. Conclusion: This study showed similar results to those of studies conducted in other countries, including having similar etiologic agents identified. The high prevalence of thyroid hyperplasia identified in this study is notable and was not reported in other, similar retrospective studies, despite being reported in locations other than Canada. Clinical relevance: Perinatal loss can have important economic consequences for horse breeders; thus, identification of the most common causes is of interest to both veterinarians and breeders.

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)0.002
Version: codex-gemma-dda1882f352aValidation 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 score0.108
Threshold uncertainty score0.643

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.071
GPT teacher head0.342
Teacher spread0.271 · 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.

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

Citations1
Published2023
Admission routes2
Has abstractyes

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