Fatigue and health-related quality of life in patients with multiple sclerosis
Bibliographic record
Abstract
Background: Multiple sclerosis is a debilitating, chronic neurological disease with diverse symptoms. Fatigue is a major aspect of this, impacting negatively on physical functioning, productivity, general well-being and health-related quality of life (HRQoL). Aim: To expose the relationship between fatigue and HRQoL in this clinical population in Saudi Arabia, supporting the development of comprehensive nursing management regimes. Methods: Patients were recruited from out-patient clinics in three Saudi Arabian cities (130 women, 71 men) for a correlational, cross-sectional study. SF-36 Health Survey and Fatigue Severity Scale were used, together with demographic variables. Descriptive analysis, correlation and t -test were applied within IBM Statistics v22. Results: Mean total Fatigue Severity Scale score was 5.59 (SD 1.18). Mean total Quality of Life score was 43.69 (SD 25.97). Fatigue was the major manifestation of the disease impacting negatively on patients’ quality of life. Conclusion: The findings not only linked fatigue to lower quality of life but also addressed the specific national demographic: an unusual pattern of significantly increasing prevalence, especially among females and young, well-educated populations. Screening this population routinely for fatigue is vital to optimise assessment, care and review of the effectiveness of nursing interventions, ultimately promoting productivity and enhancing HRQoL.
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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 | 0.001 | 0.004 |
| 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) | 0.002 | 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".