The Hidden Burden of Craniosynostosis in Brazil’s Unified Health System: A 10-Year Retrospective Analysis of the Disease’s Diagnoses and Surgical Operations
Bibliographic record
Abstract
BACKGROUND: Patients with craniosynostosis, a congenital anomaly involving early cranial suture fusion, face numerous challenges in low-income countries and middle-income countries. This study analyzed craniosynostosis diagnoses and operations in Brazil's Unified Health System (SUS) and identified associated predictors. METHODS: The authors examined craniosynostosis diagnoses and operations from 2012 to 2022 using the DATASUS database. With an estimated incidence of 1 in 2500 live births, the authors forecasted expected diagnosis volumes and utilized a decision-tree model to distinguish between syndromic and nonsyndromic diagnoses. The authors predicted the necessary operations for each group and evaluated workforce capacity trends in neurosurgery and plastic surgery. The authors assessed the implementation impact of the 2015 National Policy for Comprehensive Care for People with Rare Diseases. FINDINGS: From 2012 to 2022, 3337 diagnoses and 2120 operations related to craniosynostosis operations were recorded. However, the authors identified a gap of 9204 diagnoses and 2886 operations not performed in this period. The authors found that, for every 100 additional neurosurgeons, there were 9.2 more diagnoses ( P =0.012) and 5.2 more operations ( P =0.044); for plastic surgeons, each additional 100 corresponded to 5.2 more diagnoses ( P =0.016) and 3.0 more operations ( P =0.037). Diagnosis rates increased significantly postpolicy ( P <0.001), but operative rates did not ( P =0.071). INTERPRETATION: Although diagnoses and operations increased over time, Brazil lags behind forecasted needs. Optimizing integrated policy implementation and proactively incorporating rare diseases in the health care system are essential to improving access to craniosynostosis care.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".