The diffusion of knowledge about Chagas' disease
Bibliographic record
Abstract
JUSTIFICATIVA E OBJETIVOS: A doenca de Chagas esta presente nos paises da America Latina ha muitos anos. Desde a sua descricao, pelo medico brasileiro Dr. Carlos Justiniano Ribeiro Chagas, ela tem sido estudada, com o desenvolvimento de conhecimentos quanto ao seu diagnostico e tratamento. Entretanto, nos dias de hoje, em funcao da emigracao de habitantes de areas endemicas para areas nao endemicas, ela esta presente nos Estados Unidos e na Europa. O objetivo deste estudo foi rever as publicacoes que descrevem a presenca da doenca de Chagas na America do Norte e na Europa. CONTEUDO: Foi feita consulta a base de dados PubMed com enfase nas descricoes recentes da presenca da doenca de Chagas em paises onde ela nao e endemica. Estima-se que a proporcao de imigrantes provindos da America Latina infectados com a doenca de Chagas seja de 1,6% na Australia, 0,9% no Canada, 2,5% na Espanha e de 0,8% a 5% nos Estados Unidos. E descrito que existem 3000 imigrantes infectados vivendo na Italia, 6000 na Espanha e, aproximadamente, 100.000 nos Estados Unidos. Nestes paises a experiencia na prevencao, no diagnostico e no tratamento da doenca nao e a mesma daquela acumulada na America Latina. CONCLUSAO: Atualmente e significativa a presenca da A difusao dos conhecimentos sobre doenca de Chagas*
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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 | 0.010 | 0.078 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".