The Comparability of Functional Assessment of Chronic Illness Therapy - Fatigue Scores between Cancer and Systemic Sclerosis
Bibliographic record
Abstract
Purpose The functional assessment of chronic illness therapy-fatigue (FACIT-F) is commonly used to assess fatigue across diseases. The degree to which the FACIT-F demonstrates measurement equivalence across disease groups, however, is not known. The purpose of this study was to assess differential item functioning (DIF) of FACIT-F items between patients with cancer and systemic sclerosis (SSc or scleroderma). Methods Secondary analysis of FACIT-F data from cancer and SSc patients. Confirmatory factor analysis was used to assess the factor structure of the FACIT-F in cancer and SSc patients. The multiple-indicator, multiple-cause model was utilized to assess DIF, comparing responses from cancer and SSc patients. Results A unidimensional factor structure for the FACIT-F was demonstrated with the cancer (n = 1141), SSc (n = 1186), and combined samples. Statistically significant, but small-magnitude, DIF was found for four items. Compared to cancer patients with the same level of fatigue, SSc patients had lower scores (more fatigue) for item 2 ( bodily weakness), 7 ( energy), and 8 ( ability to perform daily activities); and higher scores (less fatigue) for item 9 ( need to sleep throughout the day). For the entire scale, SSc patients had 0.47 SD lower FACIT-F latent factor scores (more fatigue) than cancer patients. After correcting for DIF, there was a change of only 0.03 SD in this difference (0.44 SD lower). Conclusions Although statistically significant DIF was detected for four FACIT-F items, the magnitude was small and the effect on fatigue latent scores was minimal. Thus, FACIT-F scores can be used equivalently in cancer and 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 | 0.005 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".