A systematic comparison of fatigue levels in systemic sclerosis with general population, cancer and rheumatic disease samples
Bibliographic record
Abstract
OBJECTIVES: There are no studies of fatigue levels in patients with SSc. The objective of this study was to compare fatigue in SSc to general population samples and patients with rheumatic diseases and cancer, where fatigue has been researched extensively. METHODS: SSc patients completed the General Fatigue Index (GFI) of the Multidimensional Fatigue Inventory. A systematic review was conducted to select comparison samples. Mean GFI scores from SSc patients were compared with mean scores from comparison samples with t-tests and Bonferroni corrections (family-wise P < 0.05). RESULTS: A total of 106 SSc patients were sampled (97 females; 28 diffuse SSc; 11.9 +/- 7.9 yrs since diagnosis). Based on comparisons from the systematic review, mean GFI scores in SSc (13.3 +/- 4.6) were significantly higher (greater fatigue; P < 0.05) than in two large population samples (8.7 and 9.6) and than in two samples of cancer patients in remission (9.4 and 10.0). Scores for the SSc sample were significantly lower (less fatigue) compared with two samples of cancer patients in palliative care (16.8 and 17.0). SSc GFI scores were similar to scores from patients with RA (13.4), AS (13.0) and SLE (13.1) and to scores from six studies of cancer patients in active treatment (11.1-13.5). CONCLUSIONS: The high levels of fatigue reported in SSc were similar to patients with varying types and treatment stages of cancer and patients with other rheumatic diseases when assessed with the GFI, demonstrating that fatigue warrants greater attention in SSc.
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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 | 0.024 | 0.099 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.012 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".