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Record W4387241748

Environmental risk factors for UV-induced cutaneous neoplasia in horses: A GIS approach.

2023· article· en· W4387241748 on OpenAlexaffabout
Mayra Ramirez, Colleen Duncan, Paula A. Schaffer, Bruce Wobeser, Sheryl Magzamen

Bibliographic record

VenuePubMed · 2023
Typearticle
Languageen
FieldMedicine
TopicVeterinary Oncology Research
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsRisk factorMedicineDermatologyAltitude (triangle)HorseBiopsyVeterinary medicinePathologyBiology
DOInot available

Abstract

fetched live from OpenAlex

Objective: Ultraviolet light (UV) is a risk factor for the development of cutaneous neoplasia in many mammalian species. This study evaluated UV exposure as a risk factor of concern for the development of cutaneous neoplasia in equine species due to the significant UV exposure that may accrue over their lifetimes. Animals and samples: Neoplastic biopsy specimens from 3272 horses that were submitted over a 10-year period to the Colorado State University Diagnostic Medicine Center and to the University of Saskatchewan Western College of Veterinary Medicine and Prairie Diagnostic Services were evaluated. Procedure: This retrospective study assessed the spatial relationships between altitude, latitude, longitude, and UV maximum value and the probability of UV-induced cutaneous neoplasia. Results: Cases from areas at high altitude proved to have a higher prevalence of UV-induced cutaneous neoplasia than those from areas at lower elevations. A multivariable regression analysis demonstrated that altitude was the only factor significantly and positively associated with the diagnosis of UV-induced neoplasia. Conclusion and clinical relevance: Evidence of cutaneous neoplasia in horses and environmental factors that influence the degree of UV exposure in a geographic location may aid in diagnosis and suggest preventive measures from UV overexposure.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.215
Threshold uncertainty score0.596

Codex and Gemma teacher scores by category

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

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 head0.068
GPT teacher head0.300
Teacher spread0.232 · 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

Citations0
Published2023
Admission routes2
Has abstractyes

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