Validation of unattended portable monitoring (PM) for obstructive sleep apnea (OSA) diagnosis in Parkinson's disease (PD)
Bibliographic record
Abstract
Background:OSA is frequent in PD patients and is thought to contribute to PD non-motor symptoms. The use of PM as a diagnostic tool has not been validated in PD. Objective:To assess the feasibility and accuracy of PM in PD patients. Methods:25 selected patients with PD without known OSA had type III PM and in-laboratory polysomnography (PSG) on a separate night (within 30 days). Respiratory events on PSG were scored using Chicago criteria. The quality of PM signals (nasal pressure, saturation and pulse) was assessed and compared with age and sex-matched controls. Sensitivity and specificity of the PM respiratory disturbance index were calculated for three apnea-hypopnea index (AHI) thresholds: ≥5/h, ≥15/h, ≥30/h, and for oxygen desaturation index (ODI)≥5/h. Results: Subjects were 72% male, aged 63.8 ± 11.3yrs, with BMI 27.63 ± 3.8kg/m 2 , Hoehn and Yahr stage 2.0 ±0.92, AHI 15.2 ±15.3/h. Looking at PM signals, only nasal pressure integrity showed a significant difference with controls (88.9 ±15.3 for PD, 96.2 ±15.7% for controls; p=0,001). The quality of saturation and pulse signals showed a negative correlation with age in PD subjects (r= -0.47, p=0.027 and r=-0.46, p=0.031, respectively). There was no correlation with PD stage. Conclusion:PM is feasible in selected PD patients, although data quality is poorer than in the general population. Although specificity was high for AHI >30, the PM systematically underestimated the AHI, and had poor sensitivity. Table 1: PM accuracy in PD patients Total n=25 Sensitivity Specificity Accuracy AHI≥5/h (n=22) 82% 67% 0,802 AHI ≥ 15/h (n=17) 35% 75% 0,478 AHI ≥ 30/h (n=8) 50% 100% 0,819 ODI ≥ 5/h (n=5) 80% 70% 0,723
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".