Nocturnal blood pressure changes in patients with restless legs syndrome
Bibliographic record
Abstract
OBJECTIVES: To assess heart rate (HR) and blood pressure (BP) changes associated with periodic leg movements during sleep (PLMS) with or without EEG signs of arousal in subjects with primary restless legs syndrome (RLS). METHODS: Ten patients with RLS (4 women, aged 47.3 +/- 13.5 years) underwent one night of polysomnography along with noninvasive beat-to-beat BP monitoring. Ten PLMS with microarousals (PLMS-MA) and 10 PLMS without microarousals (PLMS-noMA) were analyzed in each subject. Systolic and diastolic BP (SBP, DBP) were measured within a 25-beat temporal window comprising 10 beats before and 15 beats after onset of each movement. PLMS-related BP changes were assessed by repeated measures one-way analysis of variance. BP changes associated with PLMS-MA and PLMS-noMA were compared by paired t-tests. Pearson correlation coefficients were used to assess the relationship between cardiovascular changes and clinical and polysomnographic variables. RESULTS: BP increased significantly in association with all PLMS (on average, SBP 22 mm Hg, DBP 11 mm Hg). BP changes associated with PLMS-MA were greater vs those associated with PLMS-noMA (p < 0.05). SBP and DBP changes increased with age and the duration of illness. CONCLUSIONS: Periodic leg movements-related repetitive nocturnal blood pressure fluctuations could contribute to the risk of cardiovascular diseases in patients with restless legs syndrome, especially in the elderly.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".