The Comparability of Functional Assessment of Chronic Illness Therapy - Fatigue Scores between Cancer and Systemic Sclerosis
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it