The efficacy of cladribine tablets in CIS patients retrospectively assigned the diagnosis of MS using modern criteria: Results from the ORACLE-MS study
Bibliographic record
Abstract
Background Multiple sclerosis (MS) diagnostic criteria have changed since the ORACLE-MS study was conducted; 223 of 616 patients (36.2%) would have met the diagnosis of MS vs clinically isolated syndrome (CIS) using the newer criteria. Objective The objective of this paper is to assess the effect of cladribine tablets in patients with a first clinical demyelinating attack fulfilling newer criteria (McDonald 2010) for MS vs CIS. Methods A post hoc analysis for subgroups of patients retrospectively classified as fulfilling or not fulfilling newer criteria at the first clinical demyelinating attack was conducted. Results Cladribine tablets 3.5 mg/kg ( n = 68) reduced the risk of next attack or three-month confirmed Expanded Disability Status Scale (EDSS) worsening by 74% vs placebo ( n = 72); p = 0.0009 in patients meeting newer criteria for MS at baseline. Cladribine tablets 5.25 mg/kg ( n = 83) reduced the risk of next attack or three-month confirmed EDSS worsening by 37%, but nominal significance was not reached ( p = 0.14). In patients who were still CIS after applying newer criteria, cladribine tablets 3.5 mg/kg ( n = 138) reduced the risk of conversion to clinically definite multiple sclerosis (CDMS) by 63% vs placebo ( n = 134); p = 0.0003. Cladribine tablets 5.25 mg/kg ( n = 121) reduced the risk of conversion by 75% vs placebo ( n = 134); p < 0.0001. Conclusions Regardless of the criteria used to define CIS or MS, 3.5 mg/kg cladribine tablets are effective in patients with a first clinical demyelinating attack. ClinicalTrials.gov registration: The ORACLE-MS study (NCT00725985).
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".