1076 Good Driving Behavior: A Reasonable Predictor Of Cpap Adherence?
Bibliographic record
Abstract
Obstructive Sleep Apnea (OSA) has been linked to potentially dangerous driving among fatigued individuals. When OSA is diagnosed, the usual treatment offered is continuous positive airway treatment (CPAP). Adherence to CPAP treatment remains a challenge. Here, we explore what characteristics and behaviors at time of diagnosis are associated with CPAP adherence 6 months later. Participants were 23 individuals between the ages of 25 and 70 (M=49.61), recruited from sleep clinics and were newly diagnosed with OSA by a sleep medicine specialist. At baseline, all participants completed questionnaires on driving behaviors (Driving Behaviour Questionnaire—DBQ), usual sleep experiences (Sleep Questionnaire—SQ) and general driving (General Driving Information Form—GDIF). All participants were reassessed 6 months later and self-reported CPAP treatment adherence by telephone interview. A participant was considered adherent if they reported using their treatment at least 4 hours per night, at least 80% of the time, in the 6 months preceding post-treatment testing. 14 Individuals were adherent to CPAP treatment (8 females, 6 males), and 9 individuals were non-adherent (4 females, 5 males). At baseline, means comparisons showed that the Non-Adherent group reported more near-misses (GDIF item) in the previous year (M=2.375, SD=.518) than the Adherent group (M=1.643, SD=.497, p=.006). Also, the Non-Adherent group reported worse driving behaviors in general (DBQ total score) in the previous year (M=103.375, SD=29.061) than the Adherent group (M=79.077, SD=9.169, p=.050). Lastly, The Non-Adherent group reported having more difficulty concentrating (SQ item) in the previous month (M=6.438, SD=1.499) than the Adherent group (M=4.143, SD=1.875, p=.006). All other items were not statistically significant. This study may provide an added opportunity to identify potentially non-adherent patients in a clinical setting by inquiring them about driving experiences, with the goal of improving health and functional outcomes in all patients with OSA. The findings are preliminary to future research. FRQSC, SAAQ, FRQS.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".