The effects of clinical interventions on health-related quality of life in multiple sclerosis: a meta-analysis
Bibliographic record
Abstract
The objective is to estimate the extent to which existing health care interventions designed specifically to target health-related quality of life (HRQL) in persons with multiple sclerosis (MS) achieve this aim. The structured literature search was conducted using multiple electronic databases including Ovid MEDLINE, EMBASE, Cumulative Index to Nursing and Allied Health Literature and the Cochrane Central Register of Controlled Trial, for the years 1960 to 2011. The methodological quality of selected randomized controlled trials (RCTs) was assessed using the Cochrane Collaboration's recommended domain-based method. Effect size (ES) was used to measure the effect of each intervention on HRQL. The studies were combined using a random-effects model to account for inter-study variation. Heterogeneity was tested for using the I-test and publication bias was assessed using funnel plots and the Egger weighted regression statistic. Thirty-nine RCTs met the criteria, all with acceptable methodological quality. Six major types of interventions were identified through the search. The smallest effect was observed for self-management and complementary and alternative medicine (ES=0.2), followed by medication (ES=0.3) then cognitive training and exercise (ES=0.4), and psychological interventions to improve mood (ES=0.7). The magnitude of positive effect on HRQL varied between the different types of interventions. The extent to which interventions are able to improve HRQL depends on delivering a potent intervention to those persons who have the potential to benefit.
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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 | 0.038 | 0.065 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.029 | 0.067 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".