Characterization of pica and chewing behaviors in privately owned cats: a case-control study
Bibliographic record
Abstract
OBJECTIVES: The aim of this study was to characterize pica behavior in cats. METHODS: Cat owners were recruited to participate in a questionnaire survey on pica behavior exhibited by their cats. Emphasis was put on the type of item ingested. Questions on early history and environment, as well as general health and gastrointestinal signs, were asked. Owners of healthy cats not showing pica were also recruited into a control group. Associations between variables and groups were statistically tested. RESULTS: Pica was directed most commonly at shoelaces or threads, followed by plastic, fabric, other items, rubber, paper or cardboard and wood. Some cats ingested specific items but only chewed others. A significant positive association was found between sucking and ingesting fabric (P = 0.002). Ad libitum feeding was significantly lower in the pica group than the control group (P = 0.01). Prevalence of self-sucking behavior was significantly higher in the pica group than the control group (P = 0.001). Cats with pica vomited significantly more often than control cats (P = 0.01). CONCLUSIONS AND RELEVANCE: Pica, the ingestion of inedible items, does not seem to be the consequence of a suboptimal environment or early weaning. Cats with pica were less commonly fed ad libitum than healthy cats. As frequently reported, pica and vomiting were related, but the causative association is not well established and thus warrants further investigation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".