A systematic review of the incidence and prevalence of cancer in multiple sclerosis
Bibliographic record
Abstract
BACKGROUND: Studies of cancer incidence and prevalence in multiple sclerosis (MS) have produced conflicting results. OBJECTIVE: To estimate the incidence and prevalence of cancer in persons with MS and review the quality of included studies. METHODS: We searched the PUBMED, SCOPUS, Web of Knowledge, and EMBASE databases, conference proceedings, and reference lists of all articles retrieved. Abstracts were screened for relevance by two reviewers. Data from included articles were captured using a standardized form, and the abstraction was verified by a second reviewer. We assessed quality of the included studies. We quantitatively assessed studies using the I (2) statistic, and conducted meta-analyses for population-based studies. RESULTS: We identified 38 studies. Estimates for incidence and prevalence varied substantially for most cancers. In population-based studies, cervical, breast, and digestive cancers had the highest incidence. The risk of meningiomas and urinary system cancers appeared higher than expected, while the risks of pancreatic, ovarian, prostate and testicular cancer were lower than expected. CONCLUSION: The complexity of understanding cancer risk in MS is augmented by inconsistencies in study design, and the relative paucity of age, sex and ethnicity-specific risk estimates from which the strong impact of age on the incidence of cancers can be assessed.
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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 | 0.010 | 0.048 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.007 |
| Bibliometrics | 0.016 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".