Analysis of a Survey Database of Pet Food Induced Poisoning in North America.
Bibliographic record
Abstract
Following the outbreak of pet food-induced nephrotoxicity in March 2007, a voluntary online survey of all AAVLD-accredited laboratories, commercial laboratories, and veterinary clinics across North America was conducted. There was no information on toxicity of melamine or factors affecting the disease outcome following exposure to melamine in pets. Data were collected from affected pets to learn about the disease outcome and the affected pet population. The web-based electronic survey used the online tool, SurveyMonkey™. Data were collected between April 5 and October 31, 2007. Four hundred fifty-one cases of 586 reported cases met the criteria for inclusion in the study. Most reported cases were from California, Texas, Michigan, Florida, and Ontario. Of the 451 cases, 424 were reported as affected. Of these, 278 cases (65.6%) were cats and 146 (34.4%) were dogs. A total of 278 pets (171 cats and 107 dogs) were reported to have died (a ratio of 1.6:1). However, within species, there was a higher percentage of deceased dogs (73.3%) than cats (61.5%). Of the affected pet population, older male cats with preexisting disease conditions were more likely to be deceased. Analysis of the pets in this large database of naturally affected pets yielded interesting findings. It showed that more cats than dogs were affected and also that preexisting renal diseases and old age predicted the most severe outcome (death or euthanasia) than any other factors.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".