The effect of energy conservation strategies on fatigue, function, and quality of life in adults with motor neuron disease: Randomized controlled trial
Bibliographic record
Abstract
Background: Fatigue is one of the most frequent complaints in patients with motor neuron diseases (MNDs), with a significant impact on the quality of life (QOL). There is lack of enough evidence for current pharmacological or non-pharmacological treatments of fatigue in this population to be applied in clinical setting. Energy conservation strategies are one of the key interventions for fatigue management in chronic diseases. We aimed to investigate the effect of applying these techniques in the fatigue management of patients with MND. Methods: This randomized controlled trial (RCT) study was carried out on 28 patients with MND. Participants were randomly assigned to either the intervention or control group. In addition to routine treatment, patients in the intervention group participated in 3 weekly 1-hour energy conservation programs provided by an experienced occupational therapist. The Fatigue Severity Scale (FSS) score, 36-Item Short Form Survey (SF-36), and Canadian Occupational Performance Measure (COPM) were measured at baseline, immediately after the last intervention session, and one month later. Results: FSS and COPM significantly changed after the course in the intervention group (P < 0.001 and P = 0.001, respectively). Both FSS and COPM improved significantly toward the final assessment only in the intervention group. The SF-36 changes were not significant in each of the groups. Conclusion: According to the findings of the present study, using energy conservation strategies could lead to better mid-term fatigue management and occupational performance improvement, but it did not improve QOL in patients with MND.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".