113: Global Trends in the Rate of Cleft Lip and Palate: Bridging the Gap
Bibliographic record
Abstract
Orofacial clefts are the most common craniofacial malformation of the newborn. Worldwide rates vary based on ethnicity and geography. We aimed to assess trends in the rate of cleft lip and palate (CLP) in a large number of countries across several world regions. Preferred sources for data collection included national registries, regional registries, health ministries, and academic centers. When available, we captured the number of infants born with (1) cleft lip with or without palate (CL±P), and (2) isolated cleft palate (CP) from 1990 to 2013. Geographical data including national latitude, longitude and mean elevation was captured from validated sources. Annual rates per 10,000 live births were calculated and countries were grouped according to World Health Organization (WHO) regions (Americas, Europe, South East Asia, Western Pacific, Africa and Eastern Mediterranean). Data was captured from 55 countries. According to most recent data, the highest total rates of CLP were reported in Venezuela (38 cases/10,000 births), Iran (36 cases/10,000 births) and Japan (30 cases/10,000 births). In total, 64% of infants had CL±P and only eight countries reported a higher proportion of CP compared to CL±P. Preliminary analysis of temporal trends were assessed within WHO regions from 1990 to 2013. The Americas reported significant increases in the rate of CL±P from 10.3 cases/10,000 births to 12.37 cases/10,000 births (P=0.01) and total CLP from 13.5 cases/10,000 births to 15.3 cases/10,000 births (P=0.02). No other WHO region demonstrated a significant change in CLP rate. Countries at higher elevation (>1000 m) reported significantly higher rates of cleft lip ± palate than countries at lower elevation (mean rate of 12.4 versus 8.4 cases/10,000 births; P=0.02). In contrast, countries at higher elevation had significantly lower rates of cleft palate than countries at lower elevation (mean rate of 3.5 versus 5.2 cases/10,000 births; P=0.03). We conclude that the rate of CLP has remained stable throughout much of the world, with an increase noted in the Americas. Geographical variables may influence incidence rates, and should be investigated further. Analyses are limited by a paucity of data from certain regions (Africa, Eastern Mediterranean and Asia). Future efforts to develop comprehensive registries will allow for a more accurate assessment of the global burden of CLP.
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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 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".