Human hydatidosis in the district of Evora, Portugal: a clinical-epidemiological study over a quarter of a century.
Bibliographic record
Abstract
In international studies, Portugal has been included among the countries hyper endemic in hydatidosis, but this criterion is true only for the Alentejo region.To contribute to the advancement of better clinical-epidemiological knowledge of the only hyperendemic district in the country, the district of Evora.Analysis of the clinical epidemiological protocols of 612 patients suffering from hydatidosis, studied over a period of 25 years. Several parameters were selected and a statistical analysis was performed on them, using the calculation of confidence limits and the chi2 test.It was discovered that there was a greater prevalence of hydatidosis among females ( 55.7%) and in the middle age groups; an average occurrence of 25 cases per annum; greater occurrence among patients with dogs (68.5%); hydatidosis was hyperendemic in 11 of the 14 rural counties of the District, mesoendemic in 2 and hypoendemic in 1; the average incidence for the district of Evora was 12.2 cases per 100,000 inhabitants per annum (15.2 cases when the urban parishes of the county of Evora were excluded).The study showed that the district of Evora is the most hyper endemic of all, the epicenter being the county of Alandroal, which boasted one of the highest incidence of hydatidosis in the world: 50.1 cases per 100,000 inhabitants per annum.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".