PLMS and PLMW in Healthy Subjects as a Function of Age: Prevalence and Interval Distribution
Bibliographic record
Abstract
STUDY OBJECTIVES: Several studies have demonstrated a positive correlation between periodic leg movements during sleep (PLMS) and age in healthy subjects. However, little is known about periodic leg movements during wakefulness (PLMW) in this population. Although the definitions of PLMS and PLMW specify a typical intermovement interval of 20 to 40 seconds, scoring criteria allow an intermovement interval of 4 to 90 seconds. The aim of the present study was to look at the prevalence and interval distribution of PLMS and PLMW in relationship with age in a population of healthy subjects. DESIGN: Periodic leg movements were recorded during 1 night. SETTING: Sleep laboratory, Hôpital du Sacré-Coeur de Montréal. PARTICIPANTS: Sixty-seven healthy subjects aged between 5 and 76 years (32 F, 35 M). INTERVENTIONS: N/A. MEASUREMENTS AND RESULTS: The presence of PLMS was rare before the age of 40, but then the index increased dramatically. PLMW index was higher in younger subjects compared with middle-aged subjects. Interval histograms of PLMS did not revealed a clear peak in younger subjects. With advancing age, PLMS interval histograms show a peak around 15 to 35 seconds, which is not observed in younger subjects. On the other hand, despite high indexes, PLMW interval histograms do not show a clear peak for any age group. CONCLUSION: These results illustrate that interval evaluation is an important feature of the calculation of periodic movements to discriminate spontaneous motor activity from PLMS or PLMW.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".