Impacto orçamentário da incorporação do GeneXpert MTB/RIF para o diagnóstico da tuberculose pulmonar na perspectiva do Sistema Único de Saúde, Brasil, 2013-2017
Bibliographic record
Abstract
The study aimed to estimate the budget impact of GeneXpert MTB/RIF for diagnosis of tuberculosis from the perspective of the Brazilian National Program for Tuberculosis Control, drawing on a static model using the epidemiological method, from 2013 to 2017. GeneXpert MTB/RIF was compared with two diagnostic sputum smear tests. The study used epidemiological, population, and cost data, exchange rates, and databases from the Brazilian Unified National Health System. Sensitivity analysis of scenarios was performed. Incorporation of GeneXpert MTB/RIF would cost BRL 147 million (roughly USD 45 million) in five years and would have an impact of 23 to 26% in the first two years and some 11% between 2015 and 2017. The results can support Brazilian and other Latin American health administrators in planning and managing the decision on incorporating the technology.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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; both teacher heads agree on what is shown here.
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".