Gene Expression in Chronic Fatigue Syndrome
Bibliographic record
Abstract
Chronic Fatigue Syndrome (CFS) is a disorder of unknown origin likely affecting multiple physiological processes. CFS is often a diagnosis of exclusion following a history of 6 months or more where patients may experience partial to full recovery, relapse or a worsening in symptoms and hence deterioration in health (Brkic et al., 2011). The clinical manifestations include moderate to severe fatigue, muscle pain, swollen lymph nodes, headaches, impaired sleep and cognition (Fukuda et al., 1994). A diagnosis of CFS is made using questionnaires which include Centre for Disease Prevention and control criteria for CFS, the Australian, British and Canadian CFS classifications and the recently developed World Health Organisation’s International Classification of Diseases for CFS (Carruthers et al., 2011, Carruthers et al., 2003; Fukuda et al., 1994; Lloyd et al., 1990; Sharpe et al., 1991). CFS is a heterogeneous and multifactorial disorder. Mechanisms to explain the underlying factors and processes that are responsible for disease progression and symptom profile of this disorder remains to be established. However, research has demonstrated that CFS impacts the endocrine, neurological, immune and metabolic processes resulting in impaired physiological homeostasis (Brenu et al., 2010; Demitrack, 1997; Schwartz et al., 1994). While these processes are likely compromised and collectively contribute to ill health in CFS patients, CFS remains a disorder lacking a clear molecular or biochemical cause.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".