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Record W4409173755 · doi:10.1177/17449871241290435

Fatigue and health-related quality of life in patients with multiple sclerosis

2025· article· en· W4409173755 on OpenAlexaff
Ekhlas Al‐Gamal, Saba Yaseen Hyarat, Latifa Al Jaried, Ellaine Dela Rama, Muayyad Ahmad, Tony Long

Bibliographic record

VenueJournal of research in nursing · 2025
Typearticle
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsLondon Health Sciences Centre
Fundersnot available
KeywordsMedicineQuality of life (healthcare)Psychological interventionPopulationDiseaseCross-sectional studyDescriptive statisticsPhysical therapyScale (ratio)GerontologyEnvironmental healthPsychiatryNursingInternal medicine

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.048
Threshold uncertainty score0.498

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.287
GPT teacher head0.485
Teacher spread0.198 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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