Variability and predictability in sleep patterns of chronic insomniacs
Bibliographic record
Abstract
Sleep of chronic insomniacs is often characterized by extensive night-to-night variability. To date, no study has examined this variability with long series of daily sleep data. The present study examined night-to-night variability with a sample of 106 participants meeting DSM-IV diagnostic criteria for persistent primary insomnia. Participants completed daily sleep diaries for an average of 31 days (range: 18-56). Sleep efficiency, sleep onset latency and wake after sleep onset were derived from this measure. Despite evidence of extensive night variability, results showed that sleep patterns could be classified in three clusters. The first one was characterized by a high probability of having poor sleep, the second one by a low and decreasing probability, and the third one by a constant median probability of having a poor sleep, which is an unpredictable sleep pattern. In the first cluster, poor sleep was expected each night for patients with a predominance mixed insomnia including the three insomnia subtypes. In the second cluster, patients presented moderate insomnia, sleep-onset latency below the threshold level and a predominance of sleep-maintenance insomnia. In the third pattern, poor nights seemed unpredictable for patients with moderate to severe insomnia associated with the lowest proportion of sleep-maintenance insomnia. Overall, sleep was predictable for about two-thirds of individuals, whereas it was unpredictable for about one-third. These findings confirm the presence of extensive variability in the sleep of chronic insomniacs and that poor sleep may be predictable for some of them. Additional research is needed to characterize those sleep patterns in terms of clinical features and temporal course.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 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".