Pain Characteristics of People with Chronic Fatigue Syndrome
Bibliographic record
Abstract
Rebecca Marshall BSc (Hons)a, Lorna Paul PhDa, Angus K. McFadyen PhDb, Danny Rafferty HDc & Leslie Wood PhDda Rebecca Marshall, BSc (Hons), Research Assistant, and Lorna Paul, PhD, Reader in Nursing and Health Care, Division of Nursing & Health Care, Faculty of Medicine, Glasgow University, UK.b Angus K McFadyen, PhD, Reader in Health Statistics, School of Engineering & Computing, Glasgow Caledonian University, Glasgow, Scotland, UK.c Danny Rafferty, HD, Technical Research Officer, School of Health and Social Care, Glasgow Caledonian University, Glasgow, Scotland, UK.d Leslie Wood, PhD, Senior Lecturer/Head of Physiology and Pharmacology Division, School of Biological and Biomedical Sciences, Glasgow Caledonian University, Glasgow, Scotland, UK.Source of financial support from Glasgow Caledonian University and ME Research UK.ABSTRACTObjectives: Until now, there has been a lack of fundamental research into the pain experienced in chronic fatigue syndrome [CFS]. The aims of this study were to (1) investigate the pain experiences of people with CFS with a range of disability, and (2) identify specific pain characteristics of people with CFS.Methods: Fifty people were recruited, including 10 people who were severely disabled by CFS [25% Group]. Participants completed a structured interview and a series of pain assessments about their current pain, which included the McGill Pain Questionnaire [MPQ], the Pain Anxiety Symptoms Scale [PASS], and visual analog scales.Results: Muscle pain was the most reported painful symptom [68 percent]. The current pain intensity was 43.2 mm ± 20.8 mm measured on a visual analog scale. The MPQ pain rating index was 23.6 ± 10.8. The PASS total score was 37.9 ± 17.6. Thirty percent [N = 15] of participants reported the cervical spine the location of “most severe” pain, followed by the left and right scapular and right lumbar spine [N = 10 each, 20 percent each]. Further analysis indicated that those people, who were severely disabled by CFS, also experienced significantly more pain [P < 0.05].Conclusion: The results of this study provide objective data to support anecdotal and clinical reports of pain in people with CFS. Pain in people with CFS should be accepted and treated as seriously as other conditions where pain is a significant symptom. Management strategies need to be tailored to the individual requirements of patients presenting with symptoms of both fatigue and pain.
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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) | 0.003 | 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".