Bibliographic record
Abstract
OBJECTIVE: To assess the effects of glatiramer acetate and beta interferon on fatigue in multiple sclerosis. METHODS: Fatigue was measured at baseline and six months using the fatigue impact scale (FIS). Groups (glatiramer acetate and beta interferon) were evaluated for the proportion improved, using Fisher's exact test. Logistic regression analysis assessed the relation between treatment group and improvement and controlled for confounding variables. RESULTS: Six month paired FIS assessments were available for 218 patients (76% female). Ages ranged between 19 and 61 years, with 86% having relapsing-remitting disease. Glatiramer acetate was used by 61% and beta interferon by 39%. At baseline, total FIS and subscale scores were comparable in the two groups. More patients improved on glatiramer acetate than on beta interferon on total FIS (24.8% v 12.9%, p = 0.033; adjusted odds ratio = 2.36, 95% confidence interval 1.03 to 5.42), and on physical (28.6% v 14.1%, p = 0.013) and cognitive subscales (21.1% v 10.6%, p = 0.045). Logistic regression analysis confirmed the association between glatiramer acetate use and improved fatigue, after accounting for baseline group differences. CONCLUSIONS: The odds of reduced multiple sclerosis fatigue were around twice as great with glatiramer acetate treatment as with beta interferon. Confirmation of this result is required.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".