GP.02 A population-based study of “no evident disease activity” (NEDA) in multiple sclerosis
Bibliographic record
Abstract
Background: NEDA is a composite measure that may ultimately influence clinical decisions concerning switches of disease modifying therapy (DMT) for relapsing remitting multiple sclerosis (RRMS) patients. Cohort studies from MS clinics suggest NEDA is not sustained over time in most patients despite DMT but may be limited by referral bias. We investigated NEDA in a population-based RRMS cohort. Methods: We identified all incident cases of RRMS in Olmsted County from 01/01/2000-12/31/2011. Retrospective chart review was conducted to determine persistence of NEDA -following RRMS diagnosis. NEDA failure was defined as new MRI activity, relapse, or expanded disability status scale (EDSS) -worsening. Results: There were 93 incident cases of RRMS with 82 individuals having sufficient follow-up to determine persistence of NEDA. Prior to NEDA failure 44 were not on DMT, 37 were on first-tier, injectable DMT, and 1 received mitoxantrone. NEDA was maintained by 63% at 1 year, 38% at 2 years, 19% at 5 years, and 12% at 10 years. Disability measured by EDSS was no different at 10 years in patients maintaining NEDA versus those that failed NEDA at one year (p=0.3). Conclusions: Maintenance of NEDA beyond 2 years is infrequent among a population-based cohort of newly diagnosed RRMS patients and similar to prior clinic-based cohorts.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 0.001 |
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".