Triagem neonatal: o desafio de uma cobertura universal e efetiva
Bibliographic record
Abstract
Newborn screening programs (NSP) aim to detect carriers of several congenital diseases among asymptomatic infants in order to warrant effective intervention. Specimen collection is the first step of a process that should be done in an universal and timely manner. A review of coverage and time of collection was done in NSP of several countries. The search was made in various sources, from 1998 to 2008, with "neonatal screening" and "coverage" as key words. The lack of a typical study design did not allow to the rigor required for a systematic review. Data were grouped in macro-regions. Canada had coverage of 71% in 2006 while the European coverage was of 69% in 2004, with data of 38 countries. In Asia and Pacific region, there were data of 19 countries. In Middle East and North Africa, there were data of 4 countries. In Latin America, the coverage was 49% in 2005, with data of 14 countries. In Brazil, coverage was 80%. Twelve reports had information about timeliness. The conclusion is that epidemiological transition has contributed to NSP success. Developed regions had more universal and timelier collection. In Brazil, government initiative increased access to the NSP, but late collections lead to the need of educational actions and participation of professional organizations in developing specific guidelines definition.
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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 | 0.027 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".