A Prospective Study Investigating the Health Outcomes of Bitches Neutered Prepubertally or Post-Pubertally
Bibliographic record
Abstract
There are scant studies of associations between the pubertal status at neutering and subsequent health outcomes of female dogs. This study examined health data for Labrador and Golden Retriever crossbreed bitches neutered by ovariohysterectomy either before (PrePN, n = 155) or after (PostPN, n = 151) puberty using a prospective study design. Data were extracted from an electronic database containing detailed health records for all bitches. Cruciate ligament disease occurred in significantly more PrePN (n = 11) than PostPN bitches (n = 1; Yates Chi-square = 6.784, D.F. = 1, p = 0.009), and PrePN bitches had lower probabilities of remaining free from osteoarthritis (χ2 = 5.777, D.F. = 1, p = 0.016). Additionally, PrePN bitches were diagnosed with atopic dermatitis (PrePN: 3.9 ± 0.6 years, PostPN: 1.6 ± 0.3 years; Mann–Whitney U test = 90.5, p = 0.008) and perivulval dermatitis (PrePN: 4.7 ± 0.9 years, PostPN: 0.8 ± 0.1 years; Mann–Whitney U test = 40.0, p = 0.002) at significantly older ages and had lower probabilities of remaining free from otitis externa (χ2 = 7.090, D.F. = 1, p = 0.008). No associations were identified between the pubertal status at neutering and the incidence of any urogenital disease, including urinary incontinence, which was diagnosed in a small number of bitches (one PrePN and six PostPN). The results suggest that prepubertal neutering may have a detrimental effect on some future musculoskeletal and immune diseases in bitches of these crossbreeds, providing important information to support neutering policies and to help maintain optimal dog welfare.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".