Brain atrophy after bone marrow transplantation for treatment of multiple sclerosis
Bibliographic record
Abstract
BACKGROUND: A cohort of patients with poor-prognosis multiple sclerosis (MS) underwent chemotherapy-based immune ablation followed by immune reconstitution with an autologous hematopoietic stem cell transplant (IA/aHSCT). This eliminated new focal inflammatory activity, but resulted in early acceleration of brain atrophy. OBJECTIVE: We modeled the time course of whole-brain volume in 19 patients to identify the baseline predictors of atrophy and to estimate the average rate of atrophy after IA/aHSCT. METHODS: Percentage whole-brain volume changes were calculated between the baseline and follow-up magnetic resonance imaging (MRI; mean duration: 5 years). A mixed-effects model was applied using two predictors: total busulfan dose and baseline volume of T1-weighted white-matter lesions. RESULTS: Treatment was followed by accelerated whole-brain volume loss averaging 3.3%. Both the busulfan dose and the baseline lesion volume were significant predictors. The atrophy slowed progressively over approximately 2.5 years. There was no evidence that resolution of edema contributed to volume loss. The mean rate of long-term atrophy was -0.23% per year, consistent with the rate expected from normal aging. CONCLUSION: Following IA/aHSCT, MS patients showed accelerated whole-brain atrophy that was likely associated with treatment-related toxicity and degeneration of "committed" tissues. Atrophy eventually slowed to that expected from normal aging, suggesting that stopping inflammatory activity in MS can reduce secondary degeneration and atrophy.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".