World Wide Web-based survey of vaccination practices, postvaccinal reactions, and vaccine site-associated sarcomas in cats
Bibliographic record
Abstract
OBJECTIVE: To quantify incidence of vaccination practices, postvaccinal reactions, and vaccine site-associated sarcomas in cats. DESIGN: Epidemiologic survey. Animals-31,671 cats vaccinated in the United States and Canada by veterinarians with World Wide Web access. PROCEDURE: Veterinarians used secure Web-based survey forms to report data regarding administered vaccines, postvaccinal inflammatory reactions, vaccine site-associated sarcomas, and detailed information and history on each sarcoma. Data were collected from Jan 1, 1998 to Dec 31, 2000, allowing a 1- to 3-year follow-up of vaccinated cats. RESULTS: Participants reported administering 61,747 doses of vaccine to 31,671 cats; postvaccinal inflammatory reactions developed in 73 cats (11.8 reactions/10,000 vaccine doses), and qualifying vaccine site-associated sarcomas developed in 2 cats (0.63 sarcomas/10,000 cats; 0.32 sarcomas/10,000 doses of all vaccines). CONCLUSIONS AND CLINICAL RELEVANCE: These findings indicate that the incidence of vaccine site-associated sarcomas is low and is not increasing. Thoughtful consideration of the relative risks and benefits of specific vaccines remains the best means of reducing the incidence of sarcomas. It is not necessary to remove postvaccinal granulomas unless malignant behavior is apparent or they persist > 4 months.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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".