Human rabies postexposure prophylaxis and rabid terrestrial animals in Ontario, Canada: 2014–2016
Bibliographic record
Abstract
BACKGROUND: The number of rabid terrestrial animals in Ontario has decreased markedly since the 1970s and 1980s. However, the number of recommended rabies postexposure prophylaxis (RPEP) courses has not decreased proportionally. The decision to recommend RPEP for terrestrial animal exposures should be based on a risk assessment that considers the prevalence of rabies in these animals within a jurisdiction, among other factors. OBJECTIVE: To explore trends in RPEP recommendations for exposures to terrestrial animals in Ontario in relation to the recency of terrestrial animal rabies cases by public health unit (PHU) jurisdiction. METHODS: RPEP recommendation data for the 36 Ontario PHUs were obtained from the Ontario integrated Public Health Information System and animal rabies data by PHU were obtained from the Ministry of Natural Resources and Forestry. We calculated the annual RPEP recommendation rates for terrestrial animals by PHU for 2014 to 2016, and plotted the 2016 rates in relation to the year of the most recently identified rabid terrestrial animal in the PHU. RESULTS: Between 2014 and 2016, the annual RPEP recommendation rates for terrestrial animal exposures by PHU ranged from 3.0 to 35.2 per 100,000 persons, with a median of 11.9 RPEP recommendations per 100,000 persons. In 2016, ten PHUs had not identified a rabid terrestrial animal in their jurisdiction for more than15 years. Five of these PHUs had RPEP recommendation rates above the provincial median. CONCLUSION: Along with other factors, consideration of the occurrence of rabies in terrestrial animals in a jurisdiction can assist in the risk assessment of dogs, cats or ferrets that are not available for subsequent observation.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".