Examining the differential use of a North American animal poison control call center by veterinarians and the public for dog-related calls
Bibliographic record
Abstract
Tele-triage, a subset of telehealth services, is becoming increasingly common, they offer users the ability to receive credible health advice from licensed professionals in the comfort of their own home. In the field of veterinary medicine, tele-triage services have been employed since the early 2000s, but there has been little examination of how these services are used by callers. The objectives of this study were to explore how the use of an animal poison control center (APCC) tele-triage service varied between veterinarians and the public in terms of toxicant type, animal demographics, availability of veterinary services, as well as seasonal and secular trends. Data regarding dog poisoning events were obtained from the APCC of the American Society for the Prevention of Cruelty to Animals' (ASPCA). We fitted a mixed logistic regression model with random intercepts for county and state and identified associations between caller type and the following: animal characteristics (i.e., age, weight, breed-class), type of toxicant, season, year, and access to veterinary services (i.e., veterinarians per capita in the county of the caller). The model included interaction effects between season and both plant and pesticide toxicants. There was also an interaction between year and access to veterinary care. Further investigations are needed to understand how the novelty of a toxicant and the severity of clinical signs associated with a toxicant predict the type of caller, if pet demographics are associated with the caller based on medical issues or owner attitudes, and how access to veterinary care influences the use of this tele-triage service.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".