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Record W2006763800 · doi:10.1186/1746-6148-10-11

Epidemiology of musculoskeletal injuries in a population of harness Standardbred racehorses in training

2014· article· en· W2006763800 on OpenAlexaff
Andrea Bertuglia, Michela Bullone, Federica Rossotto, Mauro Gasparini

Bibliographic record

VenueBMC Veterinary Research · 2014
Typearticle
Languageen
FieldVeterinary
TopicVeterinary Equine Medical Research
Canadian institutionsUniversité de MontréalCegep de Saint Hyacinthe
Fundersnot available
KeywordsMedicineMusculoskeletal injuryPoisson regressionEpidemiologyPopulationPhysical therapyIncidence (geometry)EtiologyInternal medicineEnvironmental healthPathology

Abstract

fetched live from OpenAlex

BACKGROUND: There is a substantial paucity of studies concerning musculoskeletal injuries in harness Standardbred racehorses. Specifically, little is known about the epidemiology of exercise-related musculoskeletal injuries. Most studies on this subject involve Thoroughbred racehorses, whose biomechanics and racing speed differ from Standardbred, making comparisons difficult. Here, a population of Standardbred racehorses trained at the same racecourse was studied over four years and a classification system for exercise-related musculoskeletal injuries was designed. The incidence rates of musculoskeletal injuries causing horses' withdrawal from training for 15 days or longer were investigated. A mixed-effects Poisson regression model was used to estimate musculoskeletal injury rates and to describe significance of selected risk factors for exercise-related injuries in this population. RESULTS: A total of 356 trotter racehorses from 10 different stables contributed 8961 months at risk of musculoskeletal injuries. Four-hundred-and-twenty-nine injuries were reported and classified into 16 categories, based on their aetiology and anatomical localisation. The overall exercise-related injury rate was 4.79 per 100 horse months. When considering risk factors one by one in separate univariable analyses, we obtained the following results: rates did not differ significantly between genders and classes of age, whereas one driver seemed to cause fewer injuries than the others. Racing speed and racing intensity, as well as recent medical history, seemed to be significant risk factors (p < 0.001), while being shod or unshod during racing was not. On the other hand, when pooling several risk factors in a multivariable approach, only racing intensity turned out to be significant (p < 0.001), since racing speed and the racing intensity were partially confounded, being strongly correlated to one another. CONCLUSION: Characterizing epidemiology of exercise-related musculoskeletal injuries in trotter racehorses provides baseline incidence rate values. Incidence rates of stress fracture are lower in Standardbreds compared to Thoroughbreds, whereas the opposite is true for tendon and suspensory ligament injuries. In addition to identification of risk factors for musculoskeletal injuries among Standardbred racehorses, results suggest that racing intensity seems to be a protective predictor of risk and recent medical history could be used to identify horses at risk of injury.

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)0.001
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 score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.452
GPT teacher head0.543
Teacher spread0.091 · 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

Citations62
Published2014
Admission routes1
Has abstractyes

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