Factors associated with relapses in relapsing-remitting multiple sclerosis
Bibliographic record
Abstract
BACKGROUND: The relapse is character of relapsing-remitting multiple sclerosis. The therapeutic goal is to reduce the risk of relapse. Factors associated with relapses can help to manage and prevent relapses. In addition, patients and doctors all pay attention to it. However, there are differences between studies. Our aim is to summarize factors associated with relapses in relapsing-remitting multiple sclerosis (RRMS). METHODS: PubMed, EMBASE, Web of science, Cochrane library, CNKI, Wanfang, SinoMed, and VIP were searched to identify risk factors about relapses in RRMS, which should be in cohort or case-control studies. This article was reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). The quality of studies was evaluated by the Newcastle-Ottawa Scale (NOS). Meta-analysis, subgroup and sensitivity analyses, and publication bias were all performed with Stata. This research has been registered on the international prospective register of systematic reviews (PROSPERO, CRD42019120502). RESULTS: 43 articles were included. Infection, postpartum period, risk gene, stress, and vitamin D were risk factors for relapses in RRMS. Pregnancy period was the protective factor. Among those, infection increased the risk of relapses in infection period (relative risk [RR], 2.07 [confidence interval (CI), 1.64 to 2.60]). Women in the postpartum period increased the risk of relapses compared with women before pregnancy (RR, 1.43 [CI, 1.19 to 1.72]), or women in pregnancy period (RR, 2.07 [CI, 1.49 to 2.88]). Women in the pregnancy period decreased the risk of relapses (RR, 0.56 [CI, 0.37 to 0.84]) compared with women before pregnancy. However, fewer studies, heterogeneity, and sample size were the limitations. CONCLUSION: It is reliable to adopt results about infection, pregnancy period, and postpartum period.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".