Does Resetting the Immune System Fix Multiple Sclerosis?
Bibliographic record
Abstract
Multiple sclerosis is the leading non-traumatic cause of disability in young adults, affecting up to 100,000 Canadians. This chronic inflammatory and neurodegenerative disease of the central nervous system leads to irreversible neurologic disability if inadequately controlled. Though many current medications are available that reduce inflammatory damage, most patients continue to show some evidence of disease activity and accrue disability. In this review, we discuss the role of immune ablation followed by autologous hematopoietic stem cell transplantation (AHSCT), a therapeutic option for select patients with a more aggressive disease course. By "resetting" the immune system with a variety of ablative conditioning regimens, followed by immune reconstitution, this therapy has shown a durable response in halting evidence of inflammatory activity in most patients, without the need for continued disease-modifying therapies (DMT). Since the introduction of this therapy, there have been advances in patient selection and supportive care, such that morbidity has significantly declined and treatment-related mortality is minimized. Recent phase-II trials have shown excellent results in efficacy and safety of AHSCT; however, challenges exist which require ongoing study. The future challenges include comparing the variety of AHSCT conditioning regimens with each other as well as with existing highly effective DMT; identifying patients with an aggressive disease course through novel biomarkers who may benefit the most from AHSCT; and surveillance of long-term outcomes of different treatment protocols. In select patients, replacing the immune system with AHSCT holds promise of fundamentally altering the trajectory of their aggressive disease course.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".