Side Effects That Occurred Early in People With Multiple Sclerosis During the First Year of Treatment With Cladribine Tablets: A Plain Language Summary
Bibliographic record
Abstract
People with multiple sclerosis (also shortened to MS) may have difficulties staying on treatment due to side effects. Cladribine tablets, approved for treating relapsing forms of MS, are given by mouth for four short periods over two years. The benefit of convenient dosing may be lost if side effects prevent people with MS from finishing their treatment. This is the summary of a study that examined side effects from cladribine tablets treatment in the first 12 weeks of two clinical studies called CLARITY and ORACLE-MS. Overall, 34.7% of participants who took cladribine tablets experienced drug-related side effects compared to 23.2% of participants who took placebo. Most side effects were mild and were seen in 54.8% of participants taking cladribine tablets and 59.1% taking the placebo. A low number of participants discontinued treatment due to side effects (1.6% of participants who took cladribine tablets; 1.4% of participants who took placebo). The researchers concluded that cladribine tablets are well-tolerated and people with MS are likely to complete the full treatment course. ClinicalTrials.gov NCT numbers: CLARITY study - NCT00213135 and ORACLE-MS study - NCT00725985.
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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".