Experience with tofacitinib in Canada: patient characteristics and treatment patterns in rheumatoid arthritis over 3 years
Bibliographic record
Abstract
OBJECTIVES: To describe characteristics, treatment patterns and persistence in patients with RA treated with tofacitinib, an oral Janus kinase inhibitor, in Canadian clinical practice between 1 June 2014 and 31 May 2017. METHODS: Data were obtained from the tofacitinib eXel support programme. Baseline demographics and medication history were collected via patient report/special authorization forms; reasons for discontinuation were captured by patient report. Treatment persistence was estimated using Kaplan-Meier methods, with data censored at last follow-up. Cox regression was applied to analyse baseline characteristics associated with treatment discontinuation. RESULTS: The number of patients with RA enrolled from 2014 to 2017 was 4276; tofacitinib utilization increased during that period, as did the proportion of biologic (b) DMARD-naïve patients prescribed tofacitinib. Of patients who initiated tofacitinib, 1226/3678 (33.3%) discontinued, mostly from lack of efficacy (35.7%) and adverse events (26.9%). Persistence was 62.7% and 49.6% after 1 and 2 years of treatment, respectively. Prior bDMARD experience predicted increased tofacitinib discontinuation (vs bDMARD-naïve, P < 0.001). Increased retention was associated with older age (56-65 years and >65 years vs ⩽45 years; P < 0.05), and time since diagnosis of 15 to <20 years (vs <5 years; P < 0.01). In bDMARD-naïve, post-1 bDMARD, post-2 bDMARD and post-⩾3 bDMARD patients, median survival was >730, 613, 667 and 592 days, respectively. CONCLUSION: Since 2014, tofacitinib use in Canadian patients with RA increased, especially among bDMARD-naïve/post-1 bDMARD patients. Median drug survival was ∼2 years. Likelihood of persistence increased for bDMARD-naïve (vs bDMARD-experienced) patients and those aged ⩾56 (vs ⩽45) years.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".