0729 Odds Ratio Product (ORP) from the Diagnostic PSG and Internal Locus of Control as Predictors of 6-Month CPAP Adherence
Bibliographic record
Abstract
Abstract Introduction Low adherence to CPAP is a well-documented problem and reduces the effectiveness of the treatment. This study aimed to investigate objective markers, such as ORP at diagnostic, combined with baseline subjective measures to predict adherence. ORP is a well-validated measure of sleep depth from 0 (deep sleep) to 2.5 (full wakefulness). Predicted adherence could be used to identify those who may need additional support for use of their prescribed treatment. Methods In a sample of 67 individuals referred for a Type 2 diagnostic study (Mage= 44.9 ±13.16, 39 females), 41 were prescribed CPAP (Mage= 45.80±13.40; MAHI=29.98±23.87) and had 6-month adherence reports. At the time of the diagnostic study, participants also completed questionnaires on sleep (Insomnia Severity Index (ISI), Epworth Sleepiness Scale (ESS)), beliefs on health (Health Value, Multidimensional Health Locus of Control (MHLC)), and social support for the use of CPAP. Adherence was determined by the percentage of days with use greater than 4 hours (MAdherence=52.0%±35.63). AHI, TST90, mean SPO2, ORPwake (mean ORP during wake periods), ORPNREM (mean ORP during NREM), ORP-9 (mean ORP 9 seconds post-arousal), percentage of TRT with ORP< 0.5 and >2.25, health value, CPAP support, ISI, ESS, and the 3 subscales of the MHLC were entered in an MLR model with backward elimination to predict adherence. Results The final model in the MLR was significant (F(4,41)=4.55, p=.005, R2=.331), and the significant predictors included AHI (b=.533, t=2.40, p=.022), TST90 (b=-.515, t=-2.33, p=.025), ORPNREM (b=.282, t=2.02, p=.050), and MHLC-Internal (b=.501, t=3.39, p=.002). Thus, greater use of CPAP was associated with a higher AHI, lower TST90, higher ORPNREM, and more internal locus of control. Conclusion These results replicate the utility of AHI, desaturations, and sleep depth during NREM during the diagnostic in predicting long-term adherence. These results suggest that individuals with worse sleep due to respiratory events at diagnostic will be more likely to adhere, potentially due to the subjective improvement to their sleep with CPAP. However, these results extended previous literature by finding that individuals with worse objective sleep at diagnostic, but also a greater sense of control over their own health, would be most likely to adhere. Support (if any)
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".