Timely Access for Orofacial Cleft Repairs in a Brazilian Amazon Center
Bibliographic record
Abstract
Orofacial clefts are the most common congenital craniofacial anomalies worldwide, and if not managed in a timely manner, can lead to significant complications. We aim to examine surgical timing at one of the few cleft care centers in the North region of Brazil since its foundation in 2016. This retrospective, descriptive study analyzed medical records from 2016 to 2023. We calculated the age at surgery for each time period and each primary surgery performed. We also evaluated the number of procedures performed outside the recommended age. Of the 1439 procedures performed from 2016 to 2023, 713 procedures met our inclusion criteria. Among these, 66.67% (n=188) of primary cheiloplasties, 67.80% (n=40) of primary lip adhesions, and 54.57% (n=203) of palatoplasties were performed outside the recommended time frame. Of the surgeries performed, 45.16% (n=322) were between 2016 and 2019, while 54.84% (n=391) were from 2020 to 2023. Considering procedures performed within the ideal recommended age groups, only 32.92% (n=106) were done between 2016 and 2019, in contrast to 45.01% (n=176) between 2020 and 2023. In conclusion, since the inception of the specialized center, there has been an increase in surgical volume and an improvement in their timing. However, many surgeries are still being conducted outside the recommended time frame.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".