Usefulness of obstructive sleep apnea-18 as a predictor of moderate-to-severe obstructive sleep apnea in children who have normal/inconclusive McGill oximetry score
Bibliographic record
Abstract
Context: Overnight oximetry is a screening test for pediatric obstructive sleep apnea (OSA). However, those who demonstrate normal/inconclusive test still require diagnostic polysomnography (PSG). Since PSG has a long waiting list, an adjunct simple test for the prioritization would be helpful. Aims: The aim of this study is to determine whether the OSA-18 quality of life (QoL) questionnaire could predict moderate-to-severe OSA in children with normal/inconclusive overnight oximetry. Settings and Design: The study involves a cross-sectional study at a university hospital. Subjects and Methods: Overnight PSG and QoL assessed by the Thai-Version OSA-18 were performed in snoring children with normal/inconclusive overnight oximetry. Statistical Analysis: Unpaired Student's t-test, Chi-square, and receiver operating characteristic curve analysis were used. Results: A total of 218 children (age 6.4 ± 2.5 years, 62% male) were studied. Sixty percent had moderate-to-severe OSA, while 40% had primary snoring/mild OSA. The mean total OSA-18 score was not different between the two groups. Subgroup analysis among those who never had medical treatment for OSA (n = 55) showed a higher total OSA-18 score in moderate-to-severe compared to primary snoring/mild OSA groups (80.5 ± 10.7 vs. 72.2 ± 14.4; P = 0.02). Total OSA-18 score >78 was the best cutoff value for predicting moderate-to-severe OSA (61.5% sensitivity, 80% specificity, 72.7% positive predictive value, and 69.7% negative predictive value). Combining this cutoff value with overweight/obesity did not improve its predictivity. Conclusions: We found the association between high total OSA-18 score and moderate-to-severe OSA in snoring children who had normal/inconclusive overnight oximetry and never had medical treatment for OSA. However, the best cutoff value of the score and other potential add-on parameters are still needed to be investigated.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".