Projeto de reativação e implantação do Programa de Monitoramento da Água Tratada para Hemodiálise do Estado de São Paulo, SP – Agosto de 2007
Bibliographic record
Abstract
The “Program of Renal Substitution Therapy –TRS” integrates the health agenda of the State of Sao Paulo because it is amongthe priority area of health policy, given theprogressive increase in the incidence and prevalence of chronic kidney disease every year. The high cost to maintain patients in TRS has been a major concern on the part of government agencies. Practices based on scientific evidence, available at present, show a relevant number of complications arising from acute dialysis treatment that uses water, crucial to the therapy, with bacteriological standards and physical-chemical inadequate, leading to the poisoningmetals and adverse reactions.The Program for Monitoring of Water Quality Treated for dialysis began in the State of Sao Paulo in December 1999. Preliminary data on the analyses of samples of water for dialysis, collected in the last quarter of 2007, revealed that the results were not satisfactory at 100% of services, which reinforces the need to implement measures to control risks to safeguard the quality standard the water, in accordance with applicable law, be it public or supply from alternative sources. In the last quarter of 2007, were trained 47 technicians of the Health Surveillance of state and municipal areas, which act directly on the segment of dialysis. These technicians collected 156 samples of water for analysis of the 52 services registered in VS of the Capital and Greater Sao Paulo.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".