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Record W7029049257

Human hydatidosis in the district of Evora, Portugal: a clinical-epidemiological study over a quarter of a century.

2007· article· en· W7029049257 on OpenAlexaboutno aff

Bibliographic record

VenuePortuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT) · 2007
Typearticle
Languageen
FieldArts and Humanities
TopicHistorical Philosophy and Science
Canadian institutionsnot available
Fundersnot available
KeywordsIncidence (geometry)EpidemiologyQuarter (Canadian coin)Public healthStatistical analysisPopulationEpicenter
DOInot available

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.025
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.340
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0250.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.005
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.193
GPT teacher head0.437
Teacher spread0.244 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2007
Admission routes1
Has abstractyes

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