In-Home, Self-Assembled Sleep Studies Are Useful in Diagnosing Sleep Apnea in the Elderly
Bibliographic record
Abstract
OBJECTIVES: Obstructive sleep apnea (OSA) is common and treatable among the elderly. Yet, few older adults seek evaluation for OSA at sleep disorders centers. The authors assessed the feasibility of a two-stage screening procedure for obstructive sleep apnea syndrome (OSAS) in a community-based sample of older adults. DESIGN: Prospective cohort study. SETTING: Participants' domicile (in-home) and academic sleep research center. PARTICIPANTS: There were 452 Medicare recipients residing in the greater Philadelphia metropolitan area with the complaint of daytime sleepiness. INTERVENTIONS: None. MEASUREMENTS AND RESULTS: All participants underwent in-home unattended sleep studies that recorded airflow, and standard in-laboratory polysomnography. Additional measures included symptoms of sleep apnea, body mass index, neck circumference, age, and sex. When comparing diagnostic approaches, the best-performing single-stage model was one that combined apnea symptoms with age and neck circumference. This model had an area under the receiver operating characteristic curve (AUC) of 0.774 and negative posttest probability of 1.2%. The best-performing two-stage model combined symptoms, neck circumference, age, and sex in the first stage, followed by an unattended portable study with a corresponding AUC of 0.85 and negative posttest probability of 0.5%. CONCLUSIONS: Unattended, self-assembled, in-home sleep studies recording airflow and respiratory effort are most useful if applied in tandem with clinical data, including a carefully obtained sleep history. This two-stage model is accurate in identifying severe OSAS in older adults and represents a practical diagnostic approach for older adults. Incorporating clinical data was vital and increased accuracy well above that of unattended studies of airflow and effort alone.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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 | 0.000 |
| 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".