Odds Ratio Product as a Biological Marker of Phenotypes of Insomnia
Bibliographic record
Abstract
ABSTRACT The study investigated differences in objective markers of sleep depth and identified phenotypes of insomnia. Participants were screened with the Insomnia‐Severity‐Index and clinical interviews and assigned to control ( n = 50) or insomnia ( n = 69) groups. They completed three nights of in‐laboratory overnight polysomnography. We measured the Odds Ratio Product (ORP), a continuous measure of sleep depth (0 = deep sleep, 2.5 = full wakefulness) and calculated: (a) ORP in stages Wake, NREM, REM, (b) percentage of TRT in deep sleep (ORP < 0.5) and full‐wakefulness (ORP > 2.25), (c) number/hour of sleep of transient increases in ORP to wake levels (Wake Intrusion Index [WII]), (d) gamma power, (e) frequency of alpha intrusions, (f) speed of return to deep sleep after arousals (ORP‐9). We used Latent Class Analysis to differentiate two insomnia groups with ‘Objectively Normal’ and ‘Objectively Poor’ metrics. The Objectively Poor group had higher ORP wake , ORP NREM , ORP REM , %TRT > 2.25, gamma power, alpha intrusion, WII and ORP‐9 than good sleeper (GSC) and the Objectively Normal group, illustrating evidence of hyperarousal, while the Objectively Normal group was comparable to GSC. The Objectively Poor group had higher %awake and lower TST. Both insomnia groups reported worse sleep and underestimated TST relative to GSC, despite similar objective sleep metrics in the Objectively Normal group. Using novel objective sleep metrics, we identified a subgroup of insomnia with abnormalities consistent with hyperarousal and another with no difference from GSC. Future research should test if these groups benefit from different treatment pathways and thus improve outcomes and time to determine appropriate treatments.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".