Abstract QS06: Comparison Between Oximetry and Polysomnography in Identifying Airway Obstruction in Infants with Robin Sequence
Bibliographic record
Abstract
PURPOSE: Polysomnography is the gold standard for the diagnosis of obstructive apnea in infants with Robin Sequence. However, its routine use is limited by cost and availability. The purpose of our study was to determine whether night-time pulse oximetry (oximetry), an inexpensive and widely available technology, could be used as a surrogate for polysomnography in identifying obstructive apnea in infants with Robin Sequence. METHODS: We reviewed all polysomnographies done in infants with Robin Sequence treated at the Montreal Children’s Hospital. We extracted the following standard data: Central Apnea Index and Mixed Obstructive Apnea Hypopnea Index (MOAHI) from polysomnography data and Desaturation Index (drops≥4%/hour, DI4%) from the oximetry done at the time of polysomnography. Symptoms of obstructive apnea were assessed with a standard questionnaire. A MOAHI≤5 was used to separate infants with no/mild obstruction from those with moderate/severe obstruction (MOAHI>5). RESULTS: We reviewed 39 polysomnographies (26 infants, age: 12.2 ± 4.8 months). Central apnea with a mild decrease in oxygenation was a frequent occurrence. All infants with a DI4%<7 events/hour (22 studies) and no significant snoring had no or mild obstructive apnea on polysomnography. In patients with DI4%>7 events/hour, 53% had moderate or severe obstruction. In the remaining infants, central events with desaturation predominated. CONCLUSION: In Robin Sequence, oximetry can identify those infants with no or mild obstructive apnea thereby decreasing the demand for polysomnography. With a DI4%>7 events/hour, polysomnography is required to differentiate between obstructive and central events. R. Galli: None.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".