Global birth prevalence of Robin sequence in live-born infants: a systematic review and meta-analysis
Bibliographic record
Abstract
Robin sequence (RS), a congenital disorder of jaw maldevelopment and glossoptosis, poses a substantial healthcare burden and has long-term health implications if airway obstruction is suboptimally treated. This study describes the global birth prevalence of RS and investigates whether prevalence estimates differ by geographical location, ethnicity or study data source (registry versus non-registry data). The protocol was prospectively registered with PROSPERO. Databases were searched using keywords and subject terms for “Robin sequence”, “epidemiology”, “incidence” and “birth prevalence”. Meta-analysis was performed fitting random effects models with arcsine transformation. From 34 eligible studies (n=2722 RS cases), pooled birth prevalence was 9.5 per 100 000 live births (95% CI 7.1–12.1) with statistical heterogeneity. One third of studies provided a case definition for RS and numerous definitions were used. A total of 22 countries were represented, predominantly from European populations (53% of studies). There was a trend towards higher birth prevalence in European populations and lower prevalence from registry-based studies. Only two studies reported ethnicity. This study indicates that RS occurs globally. To investigate geographical differences in prevalence, additional studies from non-European populations and reporting of ethnicity are needed. Heterogeneity of estimates may be due to variable diagnostic criteria and ascertainment methods. Recently published consensus diagnostic criteria may reduce heterogeneity among future studies.
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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 | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.028 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| 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".