A Plain Language Summary On the Effectiveness of Cladribine Tablets Compared With Other Oral Treatments for Multiple Sclerosis: Results From the Msbase Registry
Bibliographic record
Abstract
What is this summary about? Patient registries contain anonymous data from people who share the same medical condition. The MSBase registry contains information from over 80,000 people living with multiple sclerosis (MS) across 41 countries. Using information from the MSBase registry, the GLIMPSE (Generating Learnings In MultiPle SclErosis) study looked at real-life outcomes in 3475 people living with MS who were treated with cladribine tablets (Mavenclad®) compared with other oral treatments.What were the results? Results showed that people treated with cladribine tablets stayed on treatment for longer than other treatments given by mouth. They also had fewer relapses (also called flare ups of symptoms) than people who received a different oral treatment for their MS.What do the results mean? The results provide evidence that, compared with other oral treatments for MS, cladribine tablets are an effective medicine for people living with MS. This is an abstract of the Plain Language Summary of Publication article. To read the full Plain Language Summary of this article, click here to view the PDF.Link to original article here
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.005 |
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".