Incidence, Characteristics, and Outcomes of Robin Sequence: A Population-Based Analysis in the United States
Bibliographic record
Abstract
INTRODUCTION: While the literature is replete of clinical studies reporting on the Robin sequence (RS), population-based analyses are scarce with significant variability within the literature in terms of reported incidence, demographic parameters, and outcomes. The authors have conducted a 20-year population-based analysis to guide clinical practice. METHODS: A birth cohort was created from the available datasets in the Healthcare Cost and Utilization Project-Kids' Inpatient Database (HCUP-KID; 2000-2019). Robin sequence patients were identified and further stratified by syndromic status. Incidence, demographic parameters, and outcomes including mortality and tracheostomy rates were computed. A subset analysis comparing the isolated and syndromic cohorts was conducted. Data was analyzed through a χ 2 or t test. RESULTS: The incidence of RS was 5.15:10,000 (95% CI: 4.99-5.31) from a birth cohort of 7.5 million. Overall, 63.3% of the cohort was isolated RS and 36.7% had syndromic RS. Robin sequence patients had a significantly higher rate of cardiac (25.9%) and neurological (8.6%) anomalies compared with the general birth cohort and were most commonly managed in urban teaching hospitals ( P <0.0001). The pooled mortality and tracheostomy rates were 6.6% and 3.6%, respectively. Syndromic status was associated with a longer length of hospital stay (27.8 versus 13.6 d), tracheostomy rate (6.2% versus 2.1%), and mortality (14.1% versus 2.2%) compared with isolated RS ( P <0.0001). CONCLUSIONS: The true incidence of RS is likely higher than previously reported estimates. Isolated RS patients have a low associated mortality and tracheostomy rate and are typically managed in urban teaching hospitals. Syndromic status confers a higher mortality rate, tracheostomy rate, and length of stay compared with nonsyndromic counterparts.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".