Long Term Evolution of "Benign" Multiple Sclerosis Patients in the London Ontario Database (P01.138)
Bibliographic record
Abstract
Objective: Using conversion to secondary progressive (SP) multiple sclerosis (MS) as cutoff event for selecting patients with course, we tested which baseline features affects the probability of becoming no longer in the long term. Background Clinical severity of MS is extremely variable. Patients with not more than moderate disability within 10-15 years from onset are regarded as benign however, lack of consensus exists. Design/Methods: Among patients in the London Ontario database with course who had not experienced SP at 10 years from onset, binary logistic regression analysis assessed factors affecting the probability of remaining benign after 20 years. Results: Outcome at 20 years was known for 75% (n = 339/445) of those patients at 10 years from onset. Females predominated (71 %), mean age at onset was 26.8 years (S.D. 7.9) and most of patients had mono-symptomatic onset (71%) characterized by sensory disturbances (52.2%). Nearly half (166/339) had entered SP and were no longer benign. Eventually, among this subgroup 91.5% (152/166) reached DSS 6, 60.8% (101/166) DSS 8 and 16.8% (28/166) DSS 10 in 19.9, 31.2 and 49.7 mean years respectively. Female sex (OR = 1.68; p = 0.032) and younger age at disease onset (age 21-30 Vs > 30: OR = 1.77, p = 0.02; age ≤ 20 Vs > 30: OR = 3.36, p Conclusions: The onset of the SP phase is the watershed event differentiating cases. Lack of progression at 10 years from onset associated with about 50% probability of remaining 10 years later. Males and those older at disease onset had higher risk to become no longer benign. Supported by: Italian MS society. UK MS society. Disclosure: Dr. Scalfari has nothing to disclose. Dr. Neuhaus has nothing to disclose. Dr. Daumer has nothing to disclose. Dr. Muraro has nothing to disclose. Dr. Ebers has received personal compensation for activities with Bayer HealthCare Pharmaceuticals as a consultant.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".