Diagnosis of Obstructive Sleep Apnea in Parkinson’s Disease Patients: Is Unattended Portable Monitoring a Suitable Tool?
Bibliographic record
Abstract
Purpose. Obstructive sleep apnea (OSA) is frequent in Parkinson's disease (PD) and may contribute to nonmotor symptoms. Polysomnography (PSG) is the gold standard for OSA diagnosis. Unattended portable monitoring (PM) may improve access to diagnosis but has not been studied in PD. We assessed feasibility and diagnostic accuracy in PD. Methods. Selected PD patients without known OSA underwent home PM and laboratory PSG. The quality of PM signals (n = 28) was compared with matched controls. PM accuracy was calculated compared with PSG for standard apnea hypopnea index (AHI) thresholds. Results. Technical failure rate was 27.0% and airflow signal quality was lower than in controls. Sensitivity of PM was 84.0%, 36.4%, and 50.0% for AHI cut-offs of 5/h, 15/h, and 30/h, respectively, using the same cut-offs on PM. Specificity was 66.7%, 83.3%, and 100%, respectively. PM underestimated the AHI with a mean bias of 12.4/h. Discrepancy between PM and PSG was greater in those with more motor dysfunction. Conclusion. PM was adequate to "rule in" moderate or severe OSA in PD patients, but the failure rate was relatively high and signal quality poorer than in controls. PM overall underestimated the severity of OSA in PD patients, especially those with greater motor dysfunction.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".