Fatigue in amyotrophic lateral sclerosis: Frequency and associated factors
Bibliographic record
Abstract
We aimed to quantify fatigue frequency and evolution in amyotrophic lateral sclerosis (ALS), and to correlate fatigue with factors such as age, sex, educational level, disease duration, functionality, quality of life, dyspnoea, depression and sleepiness. Sixty ALS patients (test group: TG) selected by El Escorial criteria and 60 normal individuals (control group: CG) matched according to sex and age, were followed every three months, during 9 months, by means of self-report scales: Fatigue Assessment Instrument (Fatigue Severity Scale plus three qualitative subscales); ALS Functional Rating Scale; McGill Quality of Life Questionnaire; dyspnoea analogical scale; Beck Depression Inventory and Epworth Sleepiness Scale. Fatigue was reported by 83% of TG (median: 3.6, interquartile range 1.5-5.4), compared with 20% of CG (median: 1, 1-1), and was significantly greater in the TG (p<0.001, Mann-Whitney test). Fatigue severity increased by the ninth month of the study (p=0.0008, Friedman, Müller-Dunn post test). There was no correlation between fatigue and other parameters, except for an inverse correlation with age at disease onset (p=0.0395, Spearman rank correlation). In conclusion, fatigue was frequent in ALS, greater in the youngest patients and worsened during follow-up. Possibly, ALS related fatigue is an independent factor, which deserves individualized approach and treatment.
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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 | 0.000 | 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".