Risk of cancer development associated with disease-modifying therapies for multiple sclerosis: study protocol for a systematic review and meta-analysis of randomised and non-randomised studies
Bibliographic record
Abstract
BACKGROUND: The association between cancer and multiple sclerosis has long been investigated. Several studies and reviews have examined the risk of cancer among patients with multiple sclerosis treated with disease-modifying therapies (DMTs) but with conflicting results. This study will aim to investigate the association between DMTs for multiple sclerosis and subsequent cancer risk using research synthesis methods. METHODS/DESIGN: We designed and registered a study protocol for a systematic review and meta-analysis. We will include randomised and non-randomised trials, prospective or retrospective cohort studies, and case-control studies of treatment with DMTs compared with placebo, no treatment, or another active agent. The primary outcome will be the risk of cancer (all-malignant neoplasms) in association with the exposure of DMTs. Secondary outcomes will include site-specific cancers (e.g. breast cancer). Literature searches will be conducted in multiple electronic databases (from their inception onwards), including the following: PubMed/MEDLINE, EMBASE, and Cochrane Central Register of Controlled Trials (CENTRAL). Two researchers will screen all citations, full-text articles, and abstract data independently. The risk of bias (quality) of individual studies will be appraised using an appropriate tool. If feasible, we will use a two-stage approach to evidence synthesis: (1) Peto's method for meta-analysis of data from randomised trials alone; and (2) Random-effects model for meta-analysis adding data from non-randomised studies. We will calculate odds ratios and their associated 95% confidence intervals. Potential sources of heterogeneity will be explored in additional analyses (e.g. subgroups considering different DMTs individually, mechanism of action, type of control, length of follow-up, mode of treatment). DISCUSSION: This systematic review and meta-analysis of randomised and non-randomised studies will provide an updated synthesis of the risk of cancer associated with DMTs for adult patients with multiple sclerosis. This study will also examine some factors that may explain potential variations across studies. The findings will be published in a peer-reviewed journal. SYSTEMATIC REVIEW REGISTRATION: Open Science Framework ( https://osf.io/v4sez ).
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 | 0.103 | 0.152 |
| Meta-epidemiology (narrow) | 0.009 | 0.006 |
| Meta-epidemiology (broad) | 0.030 | 0.039 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.042 | 0.005 |
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".