Insomnia sufferers can tolerate laboratory REM sleep dream collection and may improve their sleep perception
Bibliographic record
Abstract
This project assessed the feasibility of in-lab dream collection in insomnia sufferers which have been limited, considering their reported sleep difficulties and heightened arousal. Eleven insomnia sufferers and 10 good sleepers underwent 5 consecutive polysomnography recording nights including 2 in-lab REM sleep full narrative dream collection. Dream recall frequency and sleep onset latency after each dream collection procedure were calculated. Analyses revealed no group effect (p=.14) for in-lab dream recall frequency (.96 for insomnia sufferers vs. .88 for good sleepers). Groups did not differ (p=.33) on sleep onset latency after dream collection. For sleep perception, insomnia individuals significantly underestimated their total sleep time at home (p≤.03) but not in the lab. Good sleepers correctly estimated their total sleep time at home but underestimated it in lab. For wake after sleep onset, insomnia sufferers correctly estimated it at home but underestimated it in the lab, while good sleepers underestimated it both at home and in the lab (p≤.02). These results suggest that in-lab full length dream collection can be successfully conducted with insomnia sufferers. Interestingly, dream collection also appeared to contribute to the underestimation of wake after sleep onset in insomnia individuals and good sleepers. It might be that externally induced awakenings attenuate the distress related to sleep difficulties especially in insomnia sufferers, since awakenings are attributed to dream collection.
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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 | 0.002 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".