Conditional Probability of Observing a Poor Night According to Sleep Depth Among Individuals With Insomnia
Bibliographic record
Abstract
The present study aims at verifying whether the conditional probability of observing a poor night after one, two, or three consecutive poor nights is associated with sleep depth or sleep self-estimation. Online sleep diaries were collected for 21 consecutive nights from 30 adults with insomnia. Participants completed seven consecutive nights of home polysomnography starting on night 1, 8, or 15 of the sleep diary. The conditional probabilities of observing a poor night after one, two, or three consecutive poor nights were computed for each participant. Sleep depth was measured with the Odds Ratio Product. K-Means Cluster Analyses were computed to derive sleep patterns. Pearson's correlation tests and ANOVAs were conducted to verify the existence of relations with conditional probabilities of observing poor nights and characterise identified sleep patterns. The conditional probability of observing a poor night after one, two, or three consecutive poor nights increased with objective and self-reported WASO, self-reported TWT, and sleepiness before bedtime. The probability increased on sleep depth; thus, the lack of sleep depth was more pronounced. Conversely, the probability increased as subjective SE decreased. Two sleep patterns in insomnia were derived from sleep diaries. Individuals for whom the conditional probability of having consecutive poor nights is high and constant tend to exhibit a reduced sleep depth. Cortical and physiological arousal might play a key role in the development and maintenance of sleep patterns in insomnia. Sleep self-estimation does not appear to be influenced by the conditional probability of experiencing a poor night after consecutive poor nights.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".