Autologous haematopoietic stem cell transplantation as a first-line disease-modifying therapy in patients with ‘aggressive’ multiple sclerosis
Bibliographic record
Abstract
Background: Autologous haematopoietic stem cell transplantation (AHSCT) is an effective treatment for patients with multiple sclerosis (MS) who have highly active disease, despite the use of standard disease-modifying therapies (DMTs). However, the optimal time for offering AHSCT to patients with ‘aggressive’ MS is yet to be established. Objectives: The objective was to explore the safety and efficacy of AHSCT as a first-line DMT in patients with ‘aggressive’ MS. Methods: All patients with ‘aggressive’ MS who received AHSCT as a first-line DMT in five European and North American centres were retrospectively evaluated. Results: Twenty patients were identified. The median interval between diagnosis and AHSCT was 5 (1–20) months. All had multiple poor prognostic markers with a median pre-transplant Expanded Disability Status Scale (EDSS) score of 5.0 (1.5–9.5). After a median follow-up of 30 (12–118) months, the median EDSS score improved to 2.0 (0–6.5, p < 0.0001). No patient had further relapses. Three had residual magnetic resonance imaging (MRI) disease activities in the first 6 months post-transplant, but no further new or enhancing lesions were observed in subsequent scans. Conclusion: AHSCT is safe and effective as a first-line DMT in inducing rapid and sustained remission in patients with ‘aggressive’ MS.
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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 | 0.001 |
| 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".