Risk Factors for Otitis Media in Pediatric Patients With Cleft Lip and/or Cleft Palate: Systematic Review and Meta-analysis
Bibliographic record
Abstract
Objective To evaluate risk factors for otitis media (OM) in children with cleft lip and/or palate (CL/P). Design Systematic review of MEDLINE, Embase, Cochrane, CINAHL, and Web of Science using PRISMA guidelines. Meta-analysis was performed using RevMan5.4.1. Setting Eligible studies included cross-sectional, retrospective, and prospective studies. Patients/Participants Pediatric patients with CL/P who develop OM. Interventions OM risk factors including age, gender, and cleft type and size. Main Outcome Measure(s) Incidence of OM. Results Eighteen studies involving 2272 children with CL/P were included. Pooled results showed no statistically significant difference in OM incidence between male and female children with CL/P (OR: 1.16; 95% CI: 0.80-1.60; P = .44). There was no significant difference in OM incidence between patients aged 2 years and below and those aged above 2 years (OR: 2.14; 95% CI: 0.33-13.83; P = .42). Cleft lip and palate (CLP) patients demonstrated significantly higher incidences of OM than cleft palate (CP) (OR: 1.58; 95% CI: 1.09-2.29; P = .02) and cleft lip (CL) patients (OR: 8.26; 95% CI: 2.21-30.90; P = .002). CP-only patients also demonstrated significantly higher OM incidences than CL-only patients (OR: 22.30; 95% CI: 8.40-59.21; P < .00001). Moreover, CPs classified as Veau III and IV showed higher OM incidences than those classified as Veau I and II (OR: 0.32; 95% CI: 0.18-0.58; P = .0001 and OR: 0.51; 95% CI: 0.33-0.77; P = .001, respectively). Conclusion CLP and CP alone are significant risk factors for developing OM in children, but not age and sex.
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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 | 0.011 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.034 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".