Shared Odds of Borrelia and Rabies Virus Exposure in Serbia
Bibliographic record
Abstract
Lyme borreliosis (LB) is the most common tick-borne disease in Serbia and other European countries. Rabies is a fatal zoonosis distributed worldwide and is caused by the rabies virus. Professionals at risk of rabies—including veterinarians, hunters, communal service workers, and forestry workers—overlap with some professions at a higher risk of exposure to tick bites and tick-borne pathogen infections. We hypothesized that individuals identified by the public health system as at risk of rabies virus infection, and consequently vaccinated against rabies virus, also share a higher likelihood of Borrelia exposure. To test our hypothesis, a case-control study was carried out during 2019 in Serbia to determine the seroprevalence of anti-Borrelia antibodies in two case groups (individuals at risk and vaccinated against rabies virus) and a control group (individuals without risk of rabies). Individuals vaccinated against rabies following either “pre-exposure protocol” (PrEP, n = 58) or “post-exposure protocol” (PEP, n = 42) were considered as rabies risk groups and healthy blood donors (n = 30) as the control group. The results showed higher Borrelia seroprevalence in PrEP (17.2%; 10/58) and PEP (19.0%; 8/42) groups compared with the control group (6.67%; 2/30). Furthermore, odds ratio (OR) analysis showed that risk of rabies (in either the PrEP (OR = 2.91) or PEP (OR = 3.29) groups) is associated with increased odds of being seropositive to Borrelia. However, the difference in Borrelia seroprevalence between groups was not statistically significant (Chi-square (χ²) test p > 0.05). The shared odds of LB and rabies exposure found in this study suggest that, in countries where both diseases occur, the common citizen can be at risk of both diseases when in a risky habitat. These findings are important to guide physicians in targeting high-risk groups, and diagnose LB, and to guide decision-makers in targeting control and prevention measures for both infections in risk areas.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".