The Montreal Children’s Hospital Experience Managing Robin Sequence: An Analysis of Outcomes and Algorithm for Surgical Technique Selection
Bibliographic record
Abstract
BACKGROUND: The development of mandibular distraction osteogenesis (MDO) and tongue-lip adhesion (TLA) has led to significant improvements in respiratory outcomes for the Robin sequence (RS) population. Despite such advances, there continues to be debate regarding management strategies. The authors present their experience managing the RS population with insights on technique selection. METHODS: A retrospective review of RS patients treated at the senior author's institution from 2003 to 2021 was conducted. Baseline patient demographics and clinical parameters including feeding and respiratory status were recorded. Outcomes included the need for tracheostomy or tracheostomy, decannulation rates, and feeding status. Patients were evaluated through overnight oximetry and drug-induced sleep endoscopy (DISE). Outcomes were stratified according to management technique (MDO, TLA, versus conservative) and compared through statistical analysis. RESULTS: Fifty-nine RS patients were included. Twenty-eight were managed conservatively, 19 underwent MDO, 10 underwent TLA, one underwent both TLA and MDO, and one underwent tracheostomy primarily. Overall, 1.7% of the cohort required a tracheostomy and 86% achieved oral feeding after the procedure. The MDO cohort had lower Apgar scores and mean birth weight compared with the conservative and TLA cohorts ( P < 0.05). There were no statistical differences in respiratory and feeding outcomes across all three cohorts. CONCLUSIONS: A therapeutic algorithm was developed with insight into the use of DISE and risk stratification with overnight oximetry to guide procedural selection. Using this approach, safe and satisfactory respiratory outcomes were achieved with a low tracheostomy rate. Risk stratification is possible without polysomnography, and DISE is a promising tool (that requires further validation) for procedural selection in this population. CLINICAL QUESTION/LEVEL OF EVIDENCE: Therapeutic, IV.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".