Association of Periodic Limb Movements With Medication Classes
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: To investigate the association between various medication classes and the periodic limb movement index (PLMI) in a clinical cohort of adults who completed in-laboratory polysomnography. METHODS: A single, diagnostic, overnight, in-laboratory polysomnogram was completed for 3,488 patients consecutively referred from 2010 to 2015 to determine PLMI. Medication use and medical comorbidities were collected through patient questionnaires. Associations between medication classes and PLMI were ascertained using multivariable ordinal logistic regression models. RESULTS: The median age of the cohort was 56.0 years (48.2% male). After adjusting for age, sex, body mass index, relevant comorbidities, and sleep measures, the use of selective serotonin reuptake inhibitors (SSRIs) (odds ratio [OR] 1.52) and serotonin-norepinephrine reuptake inhibitors (SNRIs) (OR 1.99) was associated with increased PLMI. Conversely, gabapentinoids (OR 0.71), stimulants (OR 0.52), benzodiazepines (OR 0.79), and dopamine agonists (OR 0.38) were associated with decreased PLMI. A non-statistically significant trend for decreased PLMI with neuroleptic use was observed. No significant associations were found between PLMI and the use of antihypertensives, statins, tricyclic antidepressants, bupropion, anticoagulants, antiplatelets, modafinil, and antihistamines. DISCUSSION: The use of SSRIs and SNRIs was associated with elevated PLMI while the use of gabapentinoids, stimulants, benzodiazepines, and dopamine agonists was associated with decreased PLMI. These results can assist physicians in managing periodic limb movements in sleep (PLMS) and invite further research into the relationship between PLMS and medications with the modulating effects of dose, formulation type, and time of administration. CLASSIFICATION OF EVIDENCE: This study provides Class II evidence that SSRIs and SNRIs are associated with elevated PLMI while gabapentinoids, stimulants, benzodiazepines, and dopamine agonists are associated with decreased PLMI.
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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 | 0.003 |
| 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) | 0.002 | 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".