Cleft lip Sidedness and the Association with Additional Congenital Malformations
Bibliographic record
Abstract
Objective To investigate the association between the sidedness of orofacial clefts and additional congenital malformations. Design Linkage of a national registry of cleft births to national administrative data of hospital admissions Setting National Health Service, England Participants 2007 children born with cleft lip ± alveolus (CL ± A) and 2724 with cleft lip and palate (CLP) born between 2000 and 2012. Main outcome measure The proportion of children with ICD-10 codes for additional congenital malformations by the sidedness (left, right or bilateral) of orofacial clefts. Results For CL ± A phenotypes, there was no evidence for a difference in the prevalence of additional anomalies between left (22%, reference), right (22%, aOR 1.02, 95% CI 0.80 to 1.28; P = .90) and bilateral clefts (23%, aOR 1.09, 95% CI 0.75 to 1.57; P = .66). For CLP phenotypes, there was evidence of a lower prevalence of additional malformations in left (23%, reference) compared to right (32%, aOR 1.54, 95% CI 1.25 to 1.91; P < .001) and bilateral clefts (33%, aOR 1.64, 95% CI 1.35 to 1.99; P < .001). Conclusions The prevalence of additional congenital malformations was similar across sidedness subtypes with CL ± A phenotypes but was different for sidedness subtypes within CLP cases. These data support the hypothesis that CL ± A has a different underlying aetiology from CLP and that within the CLP phenotype, right sided CLP may lie closer in aetiology to bilateral CLP than it does to left sided 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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".