Periodic leg movements during sleep and wakefulness in narcolepsy
Bibliographic record
Abstract
The objectives of the study were to measure the prevalence of periodic leg movements during NREM and REM sleep (PLMS) and while awake (PLMW) and to assess the impact of PLMS on nocturnal sleep and daytime functioning in patients with narcolepsy. One hundred and sixty-nine patients with narcolepsy and 116 normal controls matched for age and gender were included. Narcoleptics with high and low PLMS indices were compared to assess the impact of PLMS on sleep and Multiple Sleep Latency Test (MSLT) variables. More narcoleptics than controls had a PLMS index greater than 5 per hour of sleep (67% versus 37%) and an index greater than 10 (53% versus 21%). PLMS indices were higher both in NREM and REM sleep in narcoleptic patients, but the between-group difference was greater for REM sleep. A significant increase of PLMS index was also found with aging in both narcoleptic patients and controls. PLMW indices were also significantly higher in narcoleptic patients. Patients with an elevated index of PLMS had a higher percentage of stage 1 sleep, a lower percentage of REM sleep, a lower REM efficiency and a shorter MSLT latency. The present study demonstrates a high frequency of PLMS and PLMW in narcolepsy, an association between the presence of PLMS and measures of REM sleep and daytime functioning disruption. These results suggest that PLMS represent an intrinsic feature of narcolepsy.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".