Identification of suboptimal responders to immune modulating agents and the role of mitoxantrone in worsening multiple sclerosis
Bibliographic record
Abstract
Over the past 10 years there has been tremendous progress in providing options for the treatment of multiple sclerosis (MS). In 1993, the first of four immunomodulators, interferon beta (IFNβ)-1b (Betaseron), was approved by the FDA for relapsing–remitting MS (RRMS) to reduce the rate and severity of relapses. This was rapidly followed in 1996 by FDA approval for IFNβ-1a (Avonex) and in 1997 for glatiramer acetate (GA; Copaxone). Another formulation of IFNβ-1a (Rebif) was approved in 1997 in Europe and Canada and in the United States in 2002. Mitoxantrone (Novantrone), which has both immune-suppressive and immune-modulating effects, was approved by the FDA in 2000 for worsening RRMS, secondary progressive MS, and progressive-relapsing MS. All of these disease-modifying agents (DMAs) are most effective when initiated during phases of active inflammation. There is a need for reliable and clinically employable criteria to assess individual patients’ responses to these therapies and for information on how to combine or switch agents for best effect in patients who do not respond to initial treatment with an immunomodulator. A consensus meeting of 16 neurologists specializing in the treatment of patients with MS was held in Miami, Florida on January 9–11, 2004. The title of the conference was ‘Identifying and Treating MS Patients with a Suboptimal Response to Current Therapies: A Consensus Conference.’ This monograph provides a review of the information that was presented during the meeting and summarizes the consensus discussions that occurred among the participants. The first article in this supplement is an overview of the immunopathology of MS, presented by Dr. Edward Fox of the Multiple Sclerosis Clinic of Central Texas in Austin, Texas. MS is characterized by an acute inflammatory phase that results in demyelination and produces axon loss. These components are described in detail, along with current results from MRI …
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".