Emerging tick-borne spotted fever group rickettsioses in the Balkans
Bibliographic record
Abstract
The impact of tick-borne pathogens (TBPs) on human health has increased in the last decades, since the incidence of emerging and re-emerging infectious and zoonotic tick-borne diseases has increased worldwide. Tick-borne rickettsiae of the Spotted Fever group (SFGR) are considered as emerging pathogens that can infect humans and cause a variety of non-specific clinical symptoms. Here, we report nine cases of atypical tick-borne diseases (9/460; 1.95%) that occurred over a period of four months (from 15 April 2021 to 16 August 2021) in Serbia, from which five cases were classified as confirmed SFGR infection, two cases as probable SFGR infection and two cases as suspected SFGR infection. Within cases of confirmed SFGR infection, R. helvetica was detected as the causative agent in two cases. The most common clinical finding was non-expanding persistent circular redness, followed by eschar and enlargement of regional lymph nodes, and pain at lesion site. Rickettsia outer membrane protein B (ompB) and citrate synthase (gltA) gene fragments were amplified from clinical samples and ticks attached to patients and IgG reacting with Rickettsia conorii antigen were detected in sera samples of patients, which are highly suggestive of exposure to SFGR. Surveillance and monitoring of rickettsial diseases in Serbia should continue and extended to new areas due to the increasing trend of clinical infections caused by SFGR in the country.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".