Fatigue in Chinese Patients With Amyotrophic Lateral Sclerosis: Associated Factors and Impact on Quality of Life
Bibliographic record
Abstract
OBJECTIVES: Fatigue was considered as a common symptom in amyotrophic lateral sclerosis (ALS). Previous studies about the impact of fatigue on Quality of Life (QoL) in patients with ALS were limited and inconsistent. Besides, a systematic investigation of fatigue in Chinese patients with ALS was lacking. Therefore, this study aimed to comprehensively evaluate the frequency and associated factors of fatigue and impact on QoL in Chinese patients with ALS. PARTICIPANTS AND METHODS: Probable and definitive patients with ALS and age- and gender-matched healthy controls (HCs) were consecutively recruited. The frequency of fatigue between both the groups was determined by the Fatigue Severity Scale (FSS). Disease severity, sleep quality, sleepiness, anxiety, depression, and QoL were evaluated in patients with ALS by the ALS Functional Rating Scale-revised (ALSFRS-R) and the ALS Severity Scale (ALSSS), the Pittsburgh Sleep Quality Index (PSQI), the Epworth Sleepiness Scale (ESS), the Hamilton Anxiety Rating Scale (HARS), the Hamilton Depression Rating Scale (HDRS), and the McGill Quality of Life Questionnaire (MQOL). Then, clinical characteristics of patients with ALS with fatigue were compared with those without fatigue. Lastly, associated factors of fatigue and impact on QoL in Chinese patients with ALS were assessed. RESULTS: = 0.001). Patients with ALS with fatigue scored lower on the ALSFRS-R, the ALSSS [lower extremity (LE) + upper extremity (UE)], the total ALSSS, higher in the HARS, HDRS, PSQI, ESS scores, and a poorer QoL. Daytime dysfunction and the ALSSS (LE + UE) score were associated with a higher risk of fatigue. The ALSSS (LE + UE), the FSS, age, the HARS, and the HDRS score were significantly associated with various aspects of QoL. CONCLUSION: This study has described fatigue in Chinese patients with ALS and finding daytime dysfunction and the lower ALSSS (LE + UE) were associated with a higher risk of fatigue. Also, we identified an inverse relationship of fatigue intensity with the psychological domain of QoL.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".