Can the Cancer-related Fatigue Case-definition Criteria Be Applied to Chronic Medical Illness? A Comparison between Breast Cancer and Systemic Sclerosis
Bibliographic record
Abstract
OBJECTIVE: Fatigue is a crucial determinant of quality of life across rheumatic diseases, but the lack of agreed-upon standards for identifying clinically significant fatigue hinders research and clinical management. Case definition criteria for cancer-related fatigue were proposed for inclusion in the International Classification of Diseases. The objective was to evaluate whether the cancer-related fatigue case definition performed equivalently in women with breast cancer and systemic sclerosis (SSc) and could be used to identify patients with chronic illness-related fatigue. METHODS: The cancer-related fatigue interview (case definition criteria met if ≥ 5 of 9 fatigue-related symptoms present with functional impairment) was completed by 291 women with SSc and 278 women successfully treated for breast cancer. Differential item functioning was assessed with the multiple indicator multiple cause model. RESULTS: Items 3 (concentration) and 10 (short-term memory) were endorsed significantly less often by women with SSc compared with cancer, controlling for responses on other items. Omitting these 2 items from the case definition and requiring 4 out of the 7 remaining symptoms resulted in a similar overall prevalence of cancer-related fatigue in the cancer sample compared with the original criteria (37.4% vs 37.8%, respectively), with 97.5% of patients diagnosed identically with both definitions. Prevalence of chronic illness-related fatigue was 36.1% in SSc using 4 of 7 symptoms. CONCLUSION: The cancer-related fatigue criteria can be used equivalently to identify patients with chronic illness-related fatigue when 2 cognitive fatigue symptoms are omitted. Harmonized definitions and measurement of clinically significant fatigue will advance research and clinical management of fatigue in rheumatic diseases and other conditions.
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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 | 0.012 | 0.064 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".