Autologous hematopoietic stem cell transplantation for multiple sclerosis: A current perspective
Bibliographic record
Abstract
The most effective treatment at halting inflammation in patients with highly active multiple sclerosis (MS) is immune ablation followed by autologous hematopoietic stem cell transplantation (AHSCT). Better patient selection and supportive management, as well as advances in conditioning regimens have resulted in improved safety with AHSCT. However, which comorbidities or prior therapies increase the risks associated with AHSCT still need to be determined. In addition, there is still debate as to which AHSCT conditioning regimen offers the best balance of long-term efficacy and safety. New studies comparing AHSCT with highly effective disease-modifying therapies will help to inform on the ideal placement of AHSCT in the treatment algorithm. Currently, many centers are experienced and use AHSCT to treat select patients with MS, contributing to ongoing registries and clinical trials which will help answer these questions.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".