Medication use and symptomology in North American women with myalgic encephalomyelitis/chronic fatigue syndrome
Bibliographic record
Abstract
Background: There are no known curative treatments for myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS), and current therapeutic regimens often yield inconsistent results. Despite the profound physical and mental burden experienced by those living with ME/CFS, patients often face a trial-and-error process in finding medications that offer some relief. Method: The current study surveyed 135 North American women diagnosed with ME/CFS to characterize medication use in relation to disease features, symptomology, and function. Medications were classified into 9 categories according to their primary mechanism of action and therapeutic use. Results: Participants were primarily middle-aged (47.1 ± 15.3 years) and were diagnosed for a mean duration of 8.4 ± 9.5 years (mean ± SD). Responses showed 68.6% of participants reported taking medications specifically for ME/CFS. Of those taking ME/CFS-related symptom medications, the average use was 3.0 medications per patient, with higher use in US compared to Canadian participants. Analgesic medications (31.7%) were the most frequently used, followed by psychotropic (26.4%), and immune-related medications (10.6%). These trends persisted across different symptom profiles, apart from gastrointestinal associated medication use replacing immune-related medications in those with gastrointestinal, neurological, and psychiatric symptoms. There was no significant correlation found between the number of medications used with disease duration, age, or age at diagnosis. However, a U-shaped relationship between ME/CFS-related symptom medication use and functional capacity as assessed by self-reported physical movement (hours/week) was evident. Conclusion: Our study highlights the diverse and complex patterns in pharmacological treatment regimens for ME/CFS in women, while also underscoring the need for more tailored and evidence-based therapeutic strategies to address the varied symptom profiles.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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".