Effectiveness of autologous haematopoietic stem cell transplantation versus natalizumab in progressive multiple sclerosis
Bibliographic record
Abstract
BACKGROUND: Natalizumab was not shown to modify disability in progressive multiple sclerosis (MS). This matched observational study compared the effectiveness of autologous haematopoietic stem cell transplantation (AHSCT) with natalizumab in progressive MS. METHODS: Patients with primary/secondary progressive MS from seven AHSCT MS centres and the MSBase registry, treated with AHSCT or natalizumab, were matched on a propensity score derived from sex, age, Expanded Disability Status Scale (EDSS), number of relapses 12/24 months before baseline, time from MS onset, the most effective prior therapy and country. The pairwise-censored groups were compared on hazards of 6-month confirmed EDSS worsening and improvement, relapses and annualised relapse rates (ARRs), using Andersen-Gill proportional hazards models and conditional negative binomial model. RESULTS: 39 patients treated with AHSCT (37 with secondary progressive MS, mean age 37 years, EDSS 5.7, 28% with recent disability progression, ARR 0.54 during the preceding year) were matched with 65 patients treated with natalizumab. The study found no evidence for difference in hazards of confirmed EDSS worsening (HR 1.49, 95% CI 0.70 to 3.14) and improvement (HR 1.50, 95% CI 0.22 to 10.29) between AHSCT and natalizumab over up to 4 years. The relapse activity was also similar while treated with AHSCT and natalizumab (ARR: mean±SD 0.08±0.28 vs 0.08±0.25; HR 1.05, 95% CI 0.39 to 2.82). In the AHSCT group, 3 patients experienced febrile neutropenia during mobilisation, 9 patients experienced serum sickness, 6 patients required intensive care unit admission and 36 patients experienced complications after discharge. No treatment-related deaths were reported. CONCLUSION: This study does not support the use of AHSCT to control disability in progressive MS with advanced disability and low relapse activity.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".