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Record W2092330506 · doi:10.1136/vr.157.16.470

Specific causes of morbidity among Swedish horses insured for veterinary care between 1997 and 2000

2005· article· en· W2092330506 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueVeterinary Record · 2005
Typearticle
Languageen
FieldVeterinary
TopicVeterinary Equine Medical Research
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsLamenessMedicineBreedArthritisIncidence (geometry)Veterinary medicineJoint diseaseDiseaseFetlockPopulationPrevalencePediatricsSurgeryInternal medicineAnimal scienceBiologyEnvironmental healthPathologyOsteoarthritisAlternative medicine

Abstract

fetched live from OpenAlex

The principal aim of this study was to analyse the incidence of disease due to general and more specific causes among over 100,000 horses covered by complete insurance for veterinary care by a Swedish insurance company during 1997 to 2000. The database was used to calculate the rate of cause-specific morbidity in horses of different ages, sexes and breed groups kept in different regions with different human population densities. The joints were the most commonly affected part of the body, followed by unspecified/whole body, the skin and the digestive system. The most common specific diagnosis was fetlock arthritis, followed by lameness of undefined origin, other locomotor problems, traumatic injuries to the skin, arthritis in several joints, and colic. Geldings had the highest rate of at least one disease event in the joints, unspecified/whole body, skeletal or respiratory system, whereas in the other four major systems the difference between the sexes was marginal.

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.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.669
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.001
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 head0.179
GPT teacher head0.393
Teacher spread0.214 · 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