0365 Insomnia Subtypes Before And After Cpap Treatment Of Sleep Apnea
Bibliographic record
Abstract
Difficulty initiating and maintaining sleep (DIMS) and complaints of insomnia are common among obstructive sleep apnea (OSA) patients. However, the frequency of different types of DIMS (i.e., difficulty with sleep onset, waking after sleep onset, early awakening) experienced by those with untreated OSA and the impact of CPAP treatment on different types of DIMS are relatively unknown. Participants (mean age = 54) recruited from family practice clinics completed a sleep questionnaire and underwent polysomnography (PSG). Pre-PSG data are available for those 105 who were diagnosed with OSA, including those 46 who were prescribed CPAP treatment. One and a half years after diagnosis, 20 were adherent and, for various reasons, 26 were not. Traditional adherence and DIMS criteria were used. Insomnia was defined as DIMS plus a complaint of insomnia. Of 105 participants tested pre-PSG, 27% reported sleep onset, 33% sleep maintenance, 36% early awakening problems and 43% overall DIMS. Of those with DIMS, 32% complained of insomnia; 68% did not.However, this creates an inadequate picture; results also show that the 20 CPAP adherent participants were LESS likely to have each of these sleep problems (e.g., DIMS 25%, insomnia 20%) than the 26 nonadherent participants (e.g., DIMS 52%, insomnia 38%). Moreover, although CPAP treatment resulted in improved levels of all types of DIMS (overall DIMS = 17%, insomnia = 14%), these were small reductions. The 26 nonadherent participants 1½ years later had slight increases in DIMS (56%) and insomnia (44%). Different types of DIMS are common in OSA. This is especially so among large numbers who are not adherent to CPAP, but who could benefit from insomnia therapy. Perhaps severe DIMS contributes to poorer adherence. While CPAP treatment improved DIMS and insomnia complaints, it did this in the relatively few adherent individuals who reported DIMS and insomnia. Canadian Institutes of Health Research.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".