Clinical course of multiple sclerosis and patient experiences during breast cancer treatment
Bibliographic record
Abstract
BACKGROUND: Over one-third of multiple sclerosis (MS) patients are post-menopausal women, the primary demographic affected by breast cancer. After breast cancer diagnosis, there is little information about patients' clinical experiences with both diseases. OBJECTIVE: Utilize a case series of MS patients diagnosed with breast cancer to characterize oncologic and MS trajectories, and generate novel insights about clinical considerations using qualitative analysis. METHODS: A single-center retrospective review was performed on medical record data of patients with MS and breast cancer. Thematic analysis was used to characterize experiences with the concurrent diagnoses. RESULTS: For the 43 patients identified, mean age was 56.7 years at cancer diagnosis and MS duration was 16.5 years. Approximately half were treated with MS disease modifying therapy at cancer diagnosis, and half of these subsequently discontinued or changed therapy. Altogether 14% experienced MS relapse(s) during follow-up (with 2 relapses in the first 2 years), with mean annualized relapse rate of 0.03. Cohort Expanded Disability Status Scale (EDSS) scores remained stable during follow-up. Qualitative insights unique to this population were identified regarding immunosuppression use and neurologic symptoms. CONCLUSIONS: MS relapses were infrequent, and there was modest progression during breast cancer treatment. Oncologic outcomes were comparable to non-MS patients with similarly staged cancer.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".