Can the OSA-18 Quality-of-Life Questionnaire Detect Obstructive Sleep Apnea in Children?
Bibliographic record
Abstract
BACKGROUND: Polysomnography is the best tool available for diagnosing obstructive sleep apnea (OSA) in children. However, polysomnography is relatively inaccessible and costly, and studies are needed to evaluate other diagnostic approaches. It has been suggested that the OSA-18 quality-of-life questionnaire (OSA-18) is a useful measure that could replace polysomnography. The purpose of our study was to determine if the OSA-18, is an accurate measure for the detection of moderate-to-severe OSA. PATIENTS AND METHODS: Children who were referred to our sleep laboratory for evaluation of suspected OSA and who had a nocturnal pulse oximetry study were included in our cross-sectional study. The results of the oximetry study were interpreted by using the McGill oximetry score (MOS). Abnormal scores were consistent with moderate-to-severe OSA. We analyzed demographic and medical data in addition to the OSA-18 results. We estimated sensitivity and negative predictive values for the OSA-18 to detect an abnormal MOS. We also conducted logistic regression analyses with MOS as the dependent variable and the OSA-18 score, age, gender, comorbidities, and race as independent variables. RESULTS: We studied 334 children (mean age: 4.6 years; 58% male). The OSA-18 had a sensitivity of 40% and a negative predictive value of 73% for detecting an abnormal MOS. While controlling for other variables in the regression model, for each unit increase in the OSA-18 score, the odds of having an abnormal MOS were increased by 2%. For each 1-year increase in age, the odds of having an abnormal MOS were decreased by 17%. CONCLUSIONS: Among children who are referred to a sleep laboratory, the OSA-18 does not accurately detect which children will have an abnormal MOS and cannot be used to exclude moderate-to-severe OSA. The OSA-18 should not be used in the place of objective testing to identify moderate-to-severe OSA in children.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".