YI6 MS outcomes in breast cancer survivors and the influence of chemotherapy
Bibliographic record
Abstract
Background Managing multiple sclerosis (MS) in cancer survivors is complex due to immune system interactions and the impact of treatment. Limited guidance exists on disease-modifying therapy (DMT) use during and after chemotherapy. This study examined how DMT decisions in breast cancer influence MS outcomes and whether chemotherapy itself affects MS disease activity.Methods Data were obtained from the MSBase registry, including 172 individuals with MS and breast cancer who received chemotherapy, and 229 for whom chemotherapy data were not available. Survival analyses assessed time to first relapse and confirmed disability progression (CDP) after cancer diagnosis, with DMT modelled as a time-varying covariate. Propensity score matching compared outcomes between those who received cancer chemotherapy adjuvant to their MS treatment and matched controls receiving standard MS treatment alone.Results Post-chemotherapy MS management varied, with most clinicians de-escalating or withholding DMTs. Older age was associated with lower relapse risk (HR = 0.90, 95% CI: 0.93–0.97, p = 0.001). Chemotherapy had a protective effect on time to first relapse (HR 0.58, 95% robust CI: 0.37 to 0.95, p = 0.03) compared to matched controls receiving standard MS treatment. Chemotherapy was not associated with a significant effect on CDP (HR 0.68, 95% robust CI: 0.38–1.23, p=0.06).Conclusion Chemotherapy was associated with better relapse outcomes compared to standard therapy in matched controls, supporting withholding or de-escalating DMTs during cancer treatment. DMT reinitiation should be guided by individual risk assessment and may be less indicated in older individuals, who demonstrated a lower relapse risk following chemotherapy.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".