Validation of Persian Version of Comprehensive Fatigue Assessment Battery for Multiple Sclerosis (CFAB-MS)
Bibliographic record
Abstract
Background and purpose: Fatigue is a common symptom in multiple sclerosis (MS). Available fatigue measurement tools evaluate severity or impact of fatigue and none of them can be used to guide therapists on planning fatigue management interventions. Comprehensive Fatigue Assessment Battery for Multiple Sclerosis (CFAB-MS), in addition to assessment of the fatigue, evaluates factors related to fatigue, including sleep, pain, mobility, stress, anxiety, mood and fatigue management skills. The aim of this study was to translate the tool into Persian, adapt it culturally and establish validity of this measure in people with MS in Iran. Materials and methods: After a forward-backward translation using the International Quality of Life Assessment process, the Persian-CFAB-MS was administered to 60 people with MS. The content validity and face validity of the tool was assessed by 10 therapists. Construct validity was assessed by measuring the associations between score of the Persian-CFAB-MS and Modified fatigue impact scale (MFIS), the hospital anxiety and depression scale (HADS), Pittsburg sleep quality index (PSQI), Short-Form McGill Pain Questionnaire (SF-MPQ), and WALK-12. Data was then analyzed using SPSS (version 20). Results: From the view of 10 therapists who were experts in MS, all items of the Persian-CFAB- MS were understandable and culturally acceptable to Iranians. As hypothesized the scores were significantly correlated with MFIS, HADS, PSQI, SF-MPQ and WALK-12 (ranging from 0.470 to 0.863, P<0.001), and showed satisfactory to excellent validity. Conclusion: The results illustrated evidence to support validity of the Persian-CFAB-MS in studying the reasons for fatigue in patients with MS.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".