Medical Care Situation of People with Myalgic Encephalomyelitis/Chronic Fatigue Syndrome in Germany
Bibliographic record
Abstract
Background and Objective: Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) is a severe illness with the hallmark symptom of Post-Exertional Malaise (PEM). Currently, no biomarkers or established diagnostic tests for ME/CFS exist. In Germany, it is estimated that over 300,000 people are affected by ME/CFS. Research from the United States and the UK shows that patients with ME/CFS are medically underserved, as they face barriers to medical care access and are dissatisfied with medical care. The first aim of the current research was to investigate whether patients with ME/CFS are medically underserved in Germany in terms of access to and satisfaction with medical care. Second, we aimed at providing a German-language version of the DePaul Symptom Questionnaire Short Form (DSQ-SF) as a tool for ME/CFS diagnostics and research in German-speaking countries. Materials and Methods: The current research conducted an online questionnaire study in Germany investigating the medical care situation of patients with ME/CFS. The questionnaire was completed by 499 participants who fulfilled the Canadian Consensus Criteria and reported PEM of 14 h or longer. Results: Participants frequently reported geographic and financial reasons for not using the available medical services. Furthermore, they reported low satisfaction with medical care by the physician they most frequently visited due to ME/CFS. The German version of the DSQ-SF showed good reliability, a one-factorial structure and construct validity, demonstrated by correlations with the SF-36 as a measure of functional status. Conclusions: Findings provide evidence that patients with ME/CFS in Germany are medically underserved. The German-language translation of the DSQ-SF provides a brief, reliable and valid instrument to assess ME/CFS symptoms to be used for research and clinical practice in German-speaking countries. Pathways to improve the medical care of patients with ME/CFS are discussed.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".