Conclusions on the burden of the vector and rodent-borne diseases in Europe, the USA and Canada
Bibliographic record
Abstract
The actual incidence of this group of diseases and their public health importance is not always recognized by the public, public health authorities or the medical profession. Travellers to vector-borne disease endemic areas all too frequently fail to comply with measures of personal protection to prevent contracting an infection such as malaria and often fall ill on their return from travel; frequently diagnosis is delayed as the patient may not have been asked or volunteered information about recent travel or because of a lack of famili– arity with the symptoms of tropical diseases by physicians being consulted. As a result, treatment may also be delayed with serious consequences for the patient. As regards the vector-and rodent-borne diseases endemic to Europe, the USA and Canada, the necessary clinical suspicion to ensure an accurate diagnosis must be based on awareness of distribution of the infections and the risk that travellers may have occurred or the risk that inhabitants of endemic areas face. Several of this group of vector-and rodent-borne infections have emerged in recent years as diseases of considerable and widespread importance, perhaps foremost among them Lyme disease and West Nile virus. As an example, the incidence of Lyme disease in Germany has risen to an estimated 60 000 cases a year. Overall, Lyme disease has become the most commonly reported arthropod-borne illness in American and European countries.
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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 | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 0.002 |
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".