Sustained Clinical Remission and Rate of Relapse After Tocilizumab Withdrawal in Patients with Rheumatoid Arthritis
Bibliographic record
Abstract
OBJECTIVE: Data on when to stop use of biological agents in rheumatoid arthritis (RA) are scant. We assessed the length of remission and the rate of clinical relapse in patients with RA who had to discontinue treatment with tocilizumab (TCZ) because of the ending of longterm (5 yrs) open-label clinical trials. METHODS: All patients at 2 participating centers in Mexico were in remission, defined as Disease Activity Score 28 ≤ 2.6, with no swollen joints at the time of the last TCZ infusion. Patients were followed thereafter every 8 weeks for 12 months or until relapse. Relapse was defined as the presence of ≥ 1 swollen joint. Doses of methotrexate and antiinflammatory drugs were not changed during the followup period. RESULTS: Forty-five patients were analyzed, 87% were women (mean age 52 yrs, mean disease duration 14 yrs). During the 12 months of followup, 44% of patients maintained remission. Relapses occurred in 56% of patients: 14 during the first 3 months after the last TCZ administration. Retreatment using other agents achieved low disease activity or remission. CONCLUSION: Longterm clinical remission is possible in a number of patients with RA after suspension of TCZ. This effect has also been reported with other biologic agents. Additional data are required to support recommendations for discontinuing a biological agent after achieving remission.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".