LARIISA: soluções digitais inteligentes para apoio à tomada de decisão na gestão da Estratégia de Saúde da Família
Bibliographic record
Abstract
The LARIISA collaborators group has been conducting research and development of technological solutions to support decision-making in health systems since 2009. GISSA, a cloud system resulting from the scientific and technological evolution of the LARIISA project, is among the solutions produced. This paper aims to describe the developing trend of GISSA©, a technological tool supporting the Family Health Strategy in northeastern Brazil, pointing out challenges, paths, and potentialities. This is a descriptive and exploratory study, based on secondary sources from the IBGE, INMET, SINAN, SIM, and SINASC, with quantitative analysis based on machine-learning techniques applied to create digital health microservices. Operating in the northeast and southeast regions, GISSA© provides information that qualifies health managers' decision-making process, improving the municipal health system's management.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".