Fatigue, Sleep Disturbances, and Their Influence on Quality of Life in Cervical Dystonia Patients
Bibliographic record
Abstract
Abstract Background Nonmotor symptoms (NMS) are highly prevalent in cervical dystonia (CD). In general, fatigue and sleep are important NMS that determine a decreased health‐related quality of life (HR‐QoL), but their influence in CD is unknown. The authors systematically investigated fatigue, excessive daytime sleepiness (EDS), and sleep quality in patients with CD and controls and assessed the influence of psychiatric comorbidity, pain, and dystonia motor severity. They also examined the predictors of HR‐QoL. Methods The study included 44 patients with CD and 43 matched controls. Fatigue, EDS, and sleep quality were assessed with quantitative questionnaires and corrected for depression and anxiety using analysis of covariance. The Toronto Western Spasmodic Torticollis Rating Scale and the Clinical Global Impression Scale‐jerks/tremor subscale were used to score motor severity and to assess whether motor characteristics could explain an additional part of the variation in fatigue and sleep‐related measures. HR‐QoL was determined with the RAND‐36 item Health Survey, and predictors of HR‐QoL were assessed using multiple regression. Results Fatigue scores were increased independently from psychiatric comorbidity (4.0 vs. 2.7; P < 0.01), whereas EDS (7.3 vs. 7.4; P = 0.95) and sleep quality (6.5 vs. 6.1; P = 0.73) were highly associated with depression and anxiety. In patients with CD, motor severity did not explain the variations in fatigue (change in the correlation coefficient [ΔR2] = 0.06; P = 0.15), EDS (ΔR2 = 0.00; P = 0.96), or sleep quality (ΔR2 = 0.04; P = 0.38) scores. Fatigue, EDS, psychiatric comorbidity, and pain predicted a decreased QoL. Conclusion Independent from psychiatric comorbidity and motor severity, fatigue appeared to be a primary NMS. Sleep‐related measures were highly associated with psychiatric comorbidity, but not with motor severity. Only NMS predicted HR‐QoL, which emphasizes the importance of attention to NMS in patients with CD.
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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 | 0.002 |
| 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".