Serological Survey for Antibodies to<i>Leptospira</i>in Dogs and Raccoons in Washington State
Bibliographic record
Abstract
A high number of reported canine leptospirosis cases occurred in Washington State from 2004 to 2006. This prompted a serosurvey of healthy dogs from around the state to determine the distribution of exposure risk and to provide insight into serovar epidemiology in the region. In addition, a convenience sample of sera from injured raccoons was also tested, and clinical serological data from the Washington Animal Disease Diagnostic Laboratory were examined. The proportion of dogs with an antibody titre (>or=1:100) to any serovar was 27/158 (17.1%, 95% CI 11.6-23.9), and that proportion among raccoons was 22/115 (19.1%, 95% CI 12.4-27.5) suggesting that the potential for exposure in Washington state is not uncommon. The most frequently detected serovars in healthy dogs were Autumnalis, Icterohemorrhagiae and Canicola, in clinical canine samples Autumnalis, Bratislava and Pomona were more frequent and in sick or injured raccoons Autumnalis, and Pomona were most frequently detected. Clinical canine serology demonstrated a late summer-fall seasonality that was consistent with other reports. An outbreak of canine leptospirosis occurred during 2004-2006 and was located primarily in western Washington counties, as were three reported human cases in 2005. Canine leptospirosis surveillance is an important tool for detecting human risk of exposure and may provide insights into which serovars are currently of clinical importance.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".