Measurement of fatigue in sickle cell disease: a systematic review of fatigue measures
Bibliographic record
Abstract
BACKGROUND: Sickle cell disease (SCD) is a chronic inherited blood disorder caused by abnormal haemoglobin production, affecting over seven million people worldwide. Although pain-particularly acute bone pain-is the hallmark symptom of this disease, fatigue is also a commonly observed manifestation. Fatigue is a debilitating symptom in Sickle Cell Disease (SCD) that significantly impacts quality of life. Accurate assessment of fatigue is crucial for effective disease management. However, a comprehensive analysis of fatigue assessment tools in SCD research is lacking. OBJECTIVE: This systematic literature review aims to identify and evaluate self-reported psychometric measures of fatigue used in SCD research with children, adolescents, young adults and adults. METHODS: A systematic search was conducted across six databases from 2010 to March 2024. The main inclusion criteria included peer-reviewed journal articles, patients with all SCD genotypes, studies evaluating fatigue using a self-reported psychometric measure, and studies published in English or French. The PRISMA guidelines were followed for study selection and data extraction. RESULTS: Twenty-eight studies met the inclusion criteria, reporting on 16 psychometric measures of fatigue. The most frequently used tool was the PROMIS system. Nine dimensions of fatigue were identified, including general, physical, mental, cognitive, emotional fatigue, and its impact on motivation, activity, vigour, and sleep/rest. However, the definitions of these dimensions were often unclear. Reported fatigue scores are not directly comparable due to methodological issues and variability in the assessment used. These methodological issues limit our knowledge on the prevalence of fatigue in SCD. CONCLUSION: The lack of a standardised fatigue assessment tool in SCD research hinders direct comparison of fatigue scores across studies. Future research should prioritise the development of a tailored assessment tool for SCD, considering the specific dimensions of fatigue relevant to this population. In the interim, clinicians and researchers can employ a combination of multidimensional and unidimensional tools to gain a more comprehensive understanding of patients' fatigue experiences.
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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 | 0.017 | 0.069 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.010 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".