Predictability of Sleep in Patients with Insomnia
Bibliographic record
Abstract
STUDY OBJECTIVES: To evaluate whether the night-to-night variability in insomnia follows specific predictable patterns and to characterize sleep patterns using objective sleep and clinical variables. DESIGN: Prospective observational study. SETTING: University-affiliated sleep disorders center. PARTICIPANTS: 146 participants suffering from chronic and primary insomnia. MEASUREMENTS AND RESULTS: Daily sleep diaries were completed for an average of 48 days and self-reported questionnaires once. Three nights were spent in the sleep laboratory for polysomnographic (PSG) assessment. Sleep efficiency, sleep onset latency, wake after sleep onset, and total sleep time were derived from sleep diaries and PSG. Time-series diary data were used to compute conditional probabilities of having an insomnia night after 1, 2, or 3 consecutive insomnia night(s). Conditional probabilities were submitted to a k-means cluster analysis. A 3-cluster solution was retained. One cluster included 38 participants exhibiting an unpredictable insomnia pattern. Another included 30 participants with a low and decreasing probability to have an insomnia night. The last cluster included 49 participants exhibiting a high probability to have insomnia every night. Clusters differed on age, insomnia severity, and mental fatigue, and on subjective sleep variables, but not on PSG sleep variables. CONCLUSION: These findings replicate our previous study and provide additional evidence that unpredictability is a less prevalent feature of insomnia than suggested previously in the literature. The presence of the 3 clusters is discussed in term of sleep perception and sleep homeostasis dysregulation.
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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) | 0.002 | 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".