Chronic fatigue syndromes: real illnesses that people can recover from
Bibliographic record
Abstract
The 'Oslo Chronic Fatigue Consortium' consists of researchers and clinicians who question the current narrative that chronic fatigue syndromes, including post-covid conditions, are incurable diseases. Instead, we propose an alternative view, based on research, which offers more hope to patients. Whilst we regard the symptoms of these conditions as real, we propose that they are more likely to reflect the brain's response to a range of biological, psychological, and social factors, rather than a specific disease process. Possible causes include persistent activation of the neurobiological stress response, accompanied by associated changes in immunological, hormonal, cognitive and behavioural domains. We further propose that the symptoms are more likely to persist if they are perceived as threatening, and all activities that are perceived to worsen them are avoided. We also question the idea that the best way to cope with the illness is by prolonged rest, social isolation, and sensory deprivation.Instead, we propose that recovery is often possible if patients are helped to adopt a less threatening understanding of their symptoms and are supported in a gradual return to normal activities. Finally, we call for a much more open and constructive dialogue about these conditions. This dialogue should include a wider range of views, including those of patients who have recovered from them.
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".