Seasonal Variation and Regional Distribution of Cleft Lip and Palate in Zambia
Bibliographic record
Abstract
OBJECTIVE: To assess variations in seasonality and regional distribution of orofacial clefts in babies born in Zambia. DESIGN: A retrospective chart review was done using records of all cleft procedures performed by the only plastic surgeon in Zambia (G.J.). Delivery data from the University Teaching Hospital (UTH) were also examined to estimate the birth prevalence of orofacial clefts (55,108 live births between 2001 and 2005). PATIENTS: All cleft patients operated in Zambia from 2000 to 2006 (413 patients). RESULTS: A low birth prevalence of clefts (1/4239 live births) was found using UTH delivery data. Surgical data showed no difference for the frequency of one gender over another overall (M:F ratio is 1.04; p = .70). More bilateral clefts occurred in cleft lip and palate (CLP) patients than in cleft lip (CL) patients (p < .01), and more unilateral left-sided clefts occurred in CL than in CLP patients (p = .03). The data reflected seasonal variation in month of birth of cleft lip with or without cleft palate (CL+/-P) patients (p < .01), with a peak in April and May and more births in March through August (57.2%) than in September through February (42.8%). There was regional variation in cleft births among the nine Zambian provinces (p < .01). CONCLUSIONS: This study shows seasonal variation in clefts that may be explained, at least in part, by environmental factors affecting the development of CL+/-P. Access to treatment is likely the major determinant of regional disparity in clefts. These results provide a basis for further epidemiological studies of orofacial clefts in Zambia.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".