Impacto do déficit de investimentos para o tratamento da doença de chagas no Brasil: revisão narrativa
Bibliographic record
Abstract
Objetivo: discutir sobre o impacto do déficit de investimentos para o tratamento da Doença de Chagas no Brasil. Método: trata-se de uma revisão narrativa da literatura realizada nas seguintes bases de dados: SCIELO, LILACS, BIREME e MEDLINE. A amostra final foi composta por 27 artigos científicos publicados entre o período de 2000 a 2020. Resultados: o Brasil é um dos países com maior prevalência de pacientes portadores da doença, porém pouco se avançou em pesquisas nessa área, de modo a repercutir em um baixo incentivo e investimento dos governantes e da indústria farmacêutica para a Doença de Chagas, tendo em vista a doença ser de progressão lenta e o diagnóstico e o tratamento serem tardios. Conclusão: fica evidente a falta de investimento e políticas públicas que possibilitem o diagnóstico e o tratamento precoce da doença tendo como conseqüência um déficit na qualidade de vida dos pacientes.
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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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".