Rheumatism and chronic fatigue, the two facets of post-chikungunya disease: the TELECHIK cohort study on Reunion island
Bibliographic record
Abstract
Prolonged fatigue is increasingly reported among chikungunya virus (CHIKV)-infected populations. We investigated the relationships between CHIKV exposure, long-lasting rheumatic musculoskeletal pain (LRMSP) and chronic fatigue. 1094 participants (512 CHIKV seropositive and 582 seronegative) of the TELECHIK population-based cohort were analysed considering the duration of the manifestations throughout an average 2-year follow-up. Weighted prevalence rates and prevalence ratios for LRMSP, idiopathic chronic fatigue (ICF), and chronic fatigue syndrome (CFS)-like illness, both latter syndromes adapted from Centers for Disease Control (CDC)-1994/Fukuda criteria, were compared. Population attributable fractions (PAF) were estimated to assess the contribution of CHIKV infection to each of the three phenotypes. Among 362 adult subjects who had reported either rheumatic pain or fatigue at the onset of the infection, weighted prevalence rates of LRMSP, ICF and CFS-like illness were respectively of 32.9%, 38.7% and 23.9%, and of 8.7%, 8.5% and 7.4% among initially asymptomatic peers (P < 0.01, respectively). Each of the three outcomes was highly attributable to chikungunya (PAF of 43.2%, 36.2% and 41.0%, respectively). In the sub-cohort of CHIKV-infected subjects, LRMSP, ICF and CFS-like illness, which overlapped in 70%, accounted for 53% of the chronic manifestations. In addition to rheumatic disease, chronic fatigue could be considered in caring for patients with chronic chikungunya disease.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".