Precipitation as a Possible Risk Factor for Development of Lyme Nephritis in Dogs
Bibliographic record
Abstract
To determine whether increased precipitation in the months before diagnosis is predictive of Lyme nephritis in dogs, a symmetrical bidirectional case-crossover study was performed on 87 dogs with presumptive Lyme nephritis. Cases were geographically linked to the nearest precipitation monitoring station, and monthly total precipitation for the 4 mo preceding diagnosis was compared to monthly total precipitation 1 yr before and 1 yr after. Breeds affected included Labrador retrievers (n = 31), mixed-breed dogs (n = 19), and golden retrievers (n = 10), with a possible seasonal bimodal peak in spring and fall. In age-adjusted analysis, each additional inch of precipitation 3 mo before diagnosis was associated with an increase in the odds of developing Lyme nephritis (odds ratio 1.13, 95% confidence interval 1.01-1.25). The results suggest that increased precipitation is a possible risk factor for Lyme nephritis in dogs. These findings may provide useful information for the accurate diagnosis of dogs with protein-losing nephropathy and may guide future studies of risk factors for Lyme nephritis. The methodology employed here reinforces the concept that case-crossover designs have utility beyond acute exposures and outcomes and offer an attractive alternative for studies in companion animals.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| 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 | 0.001 |
| 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".