Investigation of relationships between body weight and age among domestic cats stratified by breed and sex
Bibliographic record
Abstract
Abstract OBJECTIVE To evaluate mean body weight (BW) over the lifespan of domestic cats stratified by breed and sex (including reproductive status [neutered vs sexually intact]). ANIMALS 19,015,888 cats. PROCEDURES Electronic medical records from veterinary clinics in the United States and Canada from 1981 to 2016 were collected through links to practice management software programs and anonymized. Age, breed, sex and reproductive status, and BW measurements and measurement dates were recorded. Data were cleaned, and descriptive statistics were determined. Linear regression models were created with data for 8-year-old domestic shorthair, medium hair, and longhair (SML) cats to explore changes in BW over 3 decades (represented by the years 1995, 2005, and 2015). RESULTS 9,886,899 of 19,015,888 (52%) cats had only 1 BW on record. Mean BW for cats of the 4 most common recognized breeds (Siamese, Persian, Himalayan, and Maine Coon Cat) peaked between 6 and 10 years of age and then declined. Mean BW of SML cats peaked at 8 years and was subjectively higher for neutered than for sexually intact cats. Mean BW of neutered 8-year-old SML cats increased between 1995 and 2005 but was steady between 2005 and 2015. CONCLUSIONS AND CLINICAL RELEVANCE The large dataset for this study yielded useful information on mean BW over the lifespan of domestic cats. This could be a basis for BW management discussions during veterinary visits. A low frequency of repeated BW measurements suggested a low frequency of repeated veterinary visits, especially after 1 year of age, making engagement of cat owners in the health of their animals particularly relevant.
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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 | 0.004 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".