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Record W2793274885 · doi:10.12927/whp.2018.25443

Treatment of Nonsyndromic Cleft Lip and/or Palate in Brazil: Existing Consensus and Legislation, Scope of the Unified Health System, Inconsistencies and Future Perspectives

2018· article· en· W2793274885 on OpenAlexvenueno aff
Marcos Roberto Tovani‐Palone

Bibliographic record

VenueWorld health & population · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCleft Lip and Palate Research
Canadian institutionsnot available
Fundersnot available
KeywordsLegislationCraniofacialScope (computer science)MedicineDentistryOrthodonticsPolitical scienceLawPsychiatry

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.035
GPT teacher head0.350
Teacher spread0.315 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations8
Published2018
Admission routes1
Has abstractyes

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