Systematic review and meta-analysis of interventions for depression and anxiety in persons with multiple sclerosis
Bibliographic record
Abstract
BACKGROUND: Depression and anxiety are common in persons with multiple sclerosis (MS), and adversely affect fatigue, medication adherence, and quality of life. Though effective treatments for depression and anxiety exist in the general population, their applicability in the MS population has not been definitively established. OBJECTIVE: To determine the overall effect of psychological and pharmacological treatments for depression or anxiety in persons with MS. METHODS: We searched the Medline, EMBASE, PsycINFO, PsycARTICLES Full Text, Cochrane Central Register of Controlled Trials, CINAHL, Web of Science, and Scopus databases using systematic review methodology from database inception until March 25, 2015. Two independent reviewers screened abstracts, extracted data, and assessed risk of bias and strength of evidence. We included controlled clinical trials reporting on the effect of pharmacological or psychological interventions for depression or anxiety in a sample of persons with MS. We calculated standardized mean differences (SMD) and pooled using random effects meta-analysis. RESULTS: Of 1753 abstracts screened, 21 articles reporting on 13 unique clinical trials met the inclusion criteria. Depression severity improved in nine psychological trials of depression treatment (N=307; SMD: -0.45 (95%CI: -0.74, -0.16)). The severity of depression also improved in three pharmacological trials of depression treatment (SMD: -0.63 (N=165; 95%CI: -1.07, -0.20)). For anxiety, only a single trial examined psychological therapy for injection phobia and reported no statistically significant improvement. CONCLUSION: Pharmacological and psychological treatments for depression were effective in reducing depressive symptoms in MS. The data are insufficient to determine the effectiveness of treatments for anxiety.
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 | 0.009 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.025 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".