Treatment of Nonsyndromic Cleft Lip and/or Palate in Brazil: Existing Consensus and Legislation, Scope of the Unified Health System, Inconsistencies and Future Perspectives
Bibliographic record
Abstract
Cleft lip and/or palate (CL/P) are the most prevalent craniofacial birth defects in humans, affecting around ten and a half million people across the world and over three hundred thousand in Brazil. Of that, about 70% of the cases occur as a nonsyndromic form, while the remaining 30% are syndromic. In turn, individuals with nonsyndromic CL/P (NSCL/P) often have anatomic deformities involving the lip, alveolar ridge and palate. In this case, the treatments generally require multiple surgeries and various other health interventions throughout childhood, adolescence and adulthood. Another relevant point is that various problems regarding the treatment of NSCL/P in Brazil through the Unified Health System (SUS) have been reported. There are also many inconsistencies in this scenario, including the territorial coverage of healthcare assistance from the craniofacial centres across the country. However, very little data can be found in the scientific literature about the current situation for the treatment of NSCL/P in Brazil. Thus, the present article discusses the existing consensus and legislation, the scope of the SUS, as well as inconsistencies and future perspectives related to the treatment of these craniofacial abnormalities at a national level.
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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 | 0.022 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".