Underreporting, Prevalence, and Epidemiological Trends of Orofacial Clefts in the Brazilian Amazon Region
Bibliographic record
Abstract
Cleft lip and palate are the most common congenital anomalies of the cranial segment worldwide. Particularly in low-income and middle-income countries, these conditions are associated with increased morbidity and mortality, socioeconomic challenges, and considerable psychological and social integration difficulties for affected individuals. This study aims to evaluate the epidemiological profile of patients with these conditions treated at a newly established specialized center in the Brazilian Amazonian Region. Data were extracted from medical records at a Reference Service for Clefts and Craniofacial Anomalies in the north of Brazil, covering the period from 2016 to 2020. These were compared with data from the official epidemiological health portal of the Brazilian Ministry of Health. The study recorded a total of 852 patients, with males comprising 54.4%. The predominant type of cleft was the transforaminal cleft, which accounted for 69.4% of cases, followed by postforamen clefts at 17.3%. The left side was more frequently affected in 63% of the cases. The primary surgical intervention performed was cheiloplasty, representing 39.5% of all procedures. Notably, 52% of the patients were from the interior regions of the state. The 2019 DataSUS data indicated a prevalence of cleft lip and palate in the State of Pará of 4.26 per 10,000 live births. However, data from this single specialized hospital showed a higher prevalence of 7.58 per 10,000 live births. These results may reflect underreporting of the number of cases reported in national official data sets.
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 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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".