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Demographics and Costs of Colic in Swedish Horses

2008· article· en· W1978070106 on OpenAlexaff
Agneta Egenvall, Johanna Penell, B. N. Bonnett, J. Blix, John Pringle

Bibliographic record

VenueJournal of Veterinary Internal Medicine · 2008
Typearticle
Languageen
FieldVeterinary
TopicVeterinary Equine Medical Research
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsMedicineDemographicsFamily medicineDemography

Abstract

fetched live from OpenAlex

BACKGROUND: Colic is an important cause of morbidity and mortality in horses. In Sweden, an insurance database with diagnostic medical information is maintained on >30% of the nation's horse population. HYPOTHESIS: The objective was to describe the occurrence of colic, defined by costly veterinary care and life claims, in horses at 1 insurance company during 1997-2002. HORSES: All horses (<21 years of age) with complete insurance for veterinary care and life during the period 1997-2002 were included. METHODS: Colic was defined as conditions where the main clinical sign was abdominal pain and the problem was related to the gastrointestinal system. The analyses included measures of incidence by sex, breed group, age categories, geographical location (urban/other), survival to and survival after colic, medical cost for colic, and multivariable modeling of risk factors related to the event of colic. RESULTS: In all, 116,288 horses contributed to 341,564 horse years at risk (HYAR). There were 3,100 horses with a colic diagnosis, of which 27% were settled for life insurance. The median gross cost for veterinary care was 4,729 Swedish Kronor (SEK). The overall occurrence and mortality rate of colic was 91 and 24 events per 10,000 HYAR. Survival after colic at 1 month was 76% (95% confidence interval: 75-78%). CONCLUSIONS AND CLINICAL IMPORTANCE: The occurrence of colic varied with breed group, age, and season. The mortality rates probably reflected the true mortality of colic. The veterinary care rates most likely underestimated of the risk colic because they represent relatively costly events.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.496
Threshold uncertainty score0.807

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.143
GPT teacher head0.422
Teacher spread0.279 · 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

Citations32
Published2008
Admission routes1
Has abstractyes

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