Pain and the Alpha-Sleep Anomaly: A Mechanism of Sleep Disruption in Facioscapulohumeral Muscular Dystrophy
Bibliographic record
Abstract
OBJECTIVE: To measure the presence of the alpha-sleep anomaly in facioscapulohumeral muscular dystrophy (FSHD) and to evaluate the association between the sleep electroencephalogram (EEG) pattern and the presence of musculoskeletal pain. DESIGN: Cross-sectional study. SETTING: Sleep laboratory. SUBJECTS: Fifty-five consecutive adult FSHD patients, 26 women and 29 men, age 49.6 ± 15.1 years (range 18-76). INTERVENTIONS: Questionnaires and polysomnography. OUTCOME MEASURES: Patients were asked to indicate if in the 3 months before the sleep study they presented persisting or recurring musculoskeletal pain. Patients who reported pain were asked to fill in the Italian version of the Brief Pain Inventory and the McGill Pain questionnaire, and a 101-point visual analog scale (VAS) for pain intensity. Polysomnographic recordings were performed. EEG was analyzed by means of Fast Fourier Transform. Four power spectra bands (δ 0-4 Hz, θ 4-8 Hz, α 8-14 Hz, β 14-32 Hz) were computed. Sleep macrostructure parameters and alpha/delta EEG power ratio during non rapid eye movement (NREM) sleep were compared between patients with and without pain. RESULTS: Forty-two patients in our sample reported chronic pain. VAS mean score was 55.2 ± 23.8 (range 10-100), pain rating index score was 13.8 ± 10.2, and present pain intensity was 2.5 ± 0.8. The statistical analysis documented an increased occurrence of the alpha and beta rhythms during NREM sleep in FSHD patients with pain. Significant correlations were observed between the alpha/delta power ratio during NREM sleep and pain measures. CONCLUSIONS: Chronic musculoskeletal pain is frequent in FSHD patients, and it represents a major mechanism of sleep disruption.
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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".