Nocturnal oximetry parameters as predictors of sleep apnea severity in resource‐limited settings
Bibliographic record
Abstract
Nocturnal oximetry is an alternative modality for evaluating obstructive sleep apnea syndrome (OSAS) severity when polysomnography is not available. The Oxygen Desaturation (≥3%) Index (ODI3) and McGill Oximetry Score (MOS) are used as predictors of moderate-to-severe OSAS (apnea-hypopnea index-AHI >5 episodes/h), an indication for adenotonsillectomy. We hypothesised that ODI3 is a better predictive parameter for AHI >5 episodes/h than the MOS. All polysomnograms performed in otherwise healthy, snoring children with tonsillar hypertrophy in a tertiary hospital (November 2014 to May 2019) were analysed. The ODI3 and MOS were derived from the oximetry channel of each polysomnogram. Logistic regression was applied to assess associations of ODI3 or MOS (predictors) with an AHI >5 episodes/h (primary outcome). Receiver operating characteristic (ROC) curves and areas under ROC curves were used to compare the ODI3 and MOS as predictors of moderate-to-severe OSAS. The optimal cut-off value for each oximetry parameter was determined using Youden's index. Polysomnograms of 112 children (median [interquartile range] age 6.1 [3.9-9.1] years; 35.7% overweight) were analysed. Moderate-to-severe OSAS prevalence was 49.1%. The ODI3 and MOS were significant predictors of moderate-to-severe OSAS after adjustment for overweight, sex, and age (odds ratio [OR] 1.34, 95% confidence interval [CI] 1.19-1.51); and OR 4.10, 95% CI 2.06-8.15, respectively; p < 0.001 for both). Area under the ROC curve was higher for the ODI3 than for MOS (0.903 [95% CI 0.842-0.964] versus 0.745 [95% CI 0.668-0.821]; p < 0.001). Optimal cut-off values for the ODI3 and MOS were ≥4.3 episodes/h and ≥2, respectively. The ODI3 emerges as preferable or at least a complementary oximetry parameter to MOS for detecting moderate-to-severe OSAS in snoring children when polysomnography is not available.
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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 | 0.004 |
| 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 | 0.000 |
| 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".