Severity and worsening of fatigue among individuals with multiple sclerosis
Bibliographic record
Abstract
Background: Fatigue is associated with reduced quality of life and social participation, and poor employment outcomes. However, most studies examining fatigue are limited by small sample sizes or short follow-up periods. Objective: To characterize the natural history of fatigue. Methods: The North American Research Committee on Multiple Sclerosis Registry participants with ≥7 years of longitudinal data between 2004 and 2019 and a relapsing disease course were included. A subset of participants enrolled within 5 years of diagnosis was identified. The Fatigue Performance Scale assessed fatigue and ≥1-point increase in Fatigue Performance Scale sustained at the next survey defined fatigue worsening. Results: Of 3057 participants with longitudinal data, 944 were within 5 years of multiple sclerosis diagnosis. Most participants (52%) reported fatigue worsening during follow-up. Median time to fatigue worsening ranged from 3.5 to 5 years at lower levels of index fatigue. Fatigue worsening was associated with lower annual income, increasing disability, lower initial fatigue level, taking injectable disease-modifying therapies and increasing depression levels in the relapsing multiple sclerosis participants. Conclusion: Most multiple sclerosis participants early in their disease suffer from fatigue and at least half reported fatigue worsening over time. Understanding factors associated with fatigue may help to identify populations most at risk of fatigue worsening will be informative for the overall management of patients with multiple sclerosis.
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 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 | 0.003 |
| 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".