Preferential Associations between Oral Clefts and Other Major Congenital Anomalies
Bibliographic record
Abstract
OBJECTIVES: To identify preferential associations between oral clefts (CL = cleft lip only, CLP = cleft lip with cleft palate, CP = cleft palate) and nonoral cleft anomalies, to interpret them on clinical grounds, and, based on the patterns of associated defects, to establish whether CL and CLP are different conditions. DESIGN AND SETTINGS: Included were 1416 cleft cases (CL = 131, CLP = 565, CP = 720), among 8304 live- and stillborn infants with multiple congenital anomalies, from 6,559,028 births reported to the International Clearinghouse for Birth Defects Surveillance and Research by 15 registries between 1994 and 2004. Rates of associated anomalies were established, and multinomial logistic regressions applied to identify significant associations. RESULTS: Positive associations with clefts were observed for only a few defects, among which anencephaly, encephaloceles, club feet, and ear anomalies were the most outstanding. Anomalies negatively associated with clefts included congenital heart defects, VATER complex (vertebral defects, imperforate anus, tracheoesophageal fistula, and radial and renal dysplasia), and spina bifida. CONCLUSION: The strong association between all types of clefts and anencephaly seems to be attributable to cases with disruptions; the association between CP and club feet seems to be attributable to conditions with fetal akinesia. Some negative associations may depend on methodologic factors, while others, such as clefts with VATER components or clefts with spina bifida, may depend on biological factors. The different patterns of defects associated with CL and CLP, indicating different underlying mechanisms, suggest that CL and CLP reflect more than just variable degrees of severity, and that distinct pathways might be involved.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".