Measuring fatigue: a meta‐review
Bibliographic record
Abstract
There is a lack of validated tools to measure fatigue in patients with inflammatory skin, neuropsychiatric, and medical disorders. The use of nonvalidated tools may compromise the quality of data. The purpose of this meta-review was to evaluate existing fatigue scales commonly used to assess fatigue in other inflammatory conditions and to identify if there are scales that have been validated in dermatologic conditions. The PubMed/MEDLINE and SCOPUS databases were systematically searched from inception through March 10, 2020, in accordance with the PRISMA statement. Validated tools were identified and assessed according to their main measurement properties. The literature search identified 403 references, and eight studies were eligible and assessed in this review. The unidimensional fatigue scales included were the Functional Assessment of Chronic Illness Therapy - Fatigue (FACIT-F), Brief Fatigue Inventory, Fatigue Severity Scale, Numerical Rating Scale - Fatigue, and Visual Analog Scale - Fatigue. The multidimensional fatigue scales found were the Checklist Individual Strength, Chalder Fatigue Scale, Multidimensional Assessment of Fatigue, Multidimensional Fatigue Inventory Scale, and Piper Fatigue Scale. To measure fatigue, a brief scale with the ability to detect change is needed as there is a growing interest in evaluating this dimension of treatment response. In addition, a good content validity is also needed. From this systematic review, none of the selected scales have had content validation, even though the FACIT was validated in patients with psoriatic arthritis. Validation studies in specific disorders are urgently warranted.
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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 | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.025 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".