Commonalities in the Features of Cancer and Chronic Fatigue Syndrome (CFS): Evidence for Stress-Induced Phenotype Instability?
Bibliographic record
Abstract
Chronic Fatigue Syndrome/Myalgic Encephalomyelitis (CFS/ME) and Cancer-Related Fatigue (CRF) are syndromes with considerable overlap with respect to symptoms. There have been many studies that have compared the two conditions, and some of this research suggests that the etiologies of the conditions are linked in some cases. In this narrative review, CFS/ME and cancer are introduced, along with their known and putative mechanistic connections to multiple stressors including ionizing radiation. Next, we summarize findings from the literature that suggest the involvement of HPA-axis dysfunction, the serotonergic system, cytokines and inflammation, metabolic insufficiency and mitochondrial dysfunction, and genetic changes in CRF and CFS/ME. We further suspect that the manifestation of fatigue in both diseases and its causes could indicate that CRF and CFS/ME lie on a continuum of potential biological effects which occur in response to stress. The response to this stress likely varies depending on predisposing factors such as genetic background. Finally, future research ideas are suggested with a focus on determining if common biomarkers exist in CFS/ME patients and those afflicted with CRF. Both CFS/ME and CRF are relatively heterogenous syndromes, however, it is our hope that this review assists in future research attempting to elucidate the commonalities between CRF and CFS/ME.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".