Bibliographic record
Abstract
OBJECTIVE: The present study explores the clinical utility and sensitivity of actigraphy as an outcome measure in the treatment of chronic insomnia. DESIGN: Following a screening-adaptation night, polysomnography, actigraphy, and sleep-diary data were collected in the sleep laboratory for 2 baseline nights and 2 posttreatment nights. SETTING: A university-affiliated sleep disorders center. PARTICIPANTS: Seventeen participants with chronic primary insomnia. Mean age was 41.6 years. INTERVENTIONS: Participants took part in a treatment protocol investigating different sequential treatments for insomnia (these results are reported elsewhere). MEASUREMENTS AND RESULTS: Compared to polysomnography, both actigraphy and sleep-diary instruments underestimated total sleep time and sleep efficiency and overestimated total wake time. Also, actigraphy underestimated sleep-onset latency while the sleep diary overestimated it as compared to polysomnography. Actigraphy data were more accurate than sleep-diary data when compared to polysomnography. Finally, actigraphy was sensitive in detecting the effects of treatment on several sleep parameters. CONCLUSIONS: These results suggest that actigraphy is a useful device for measuring treatment response and that it should be used as a complement to sleep-diary evaluation.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 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".