Breed-specific incidence rates of canine primary bone tumors--a population based survey of dogs in Norway.
Why this work is in the frame
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Bibliographic record
Abstract
This is one of few published population-based studies describing breed specific rates of canine primary bone tumors. Incidence rates related to dog breeds could help clarify the impact of etiological factors such as birth weight, growth rate, and adult body weight/height on development of these tumors. The study population consisted of dogs within 4 large/giant breeds; Irish wolfhound (IW), Leonberger (LB), Newfoundland (NF), and Labrador retriever (LR), born between January 1st 1989 and December 31st 1998. Questionnaires distributed to owners of randomly selected dogs--fulfilling the criteria of breed, year of birth, and registration in the Norwegian Kennel Club--constituted the basis for this retrospective, population-based survey. Of the 3748 questionnaires received by owners, 1915 were completed, giving a response rate of 51%. Forty-three dogs had been diagnosed with primary bone tumors, based upon clinical examination and x-rays. The breeds IW and LB, with 126 and 72 cases per 10 000 dog years at risk (DYAR), respectively, had significantly higher incidence rates of primary bone tumors than NF and LR (P < 0.0001). Incidence rates for the latter were 11 and 2 cases per 10 000 DYAR, respectively. Pursuing a search for risk factors other than body size/weight is supported by the significantly different risks of developing primary bone tumors between similarly statured dogs, like NF and LB, observed in this study. Defining these breed-specific incidence rates enables subsequent case control studies, ultimately aiming to identify specific etiological factors for developing primary bone tumors.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it