Persistence of Biologics in the Treatment of Psoriatic Arthritis: Data From a Large <scp>Hospital‐Based</scp> Longitudinal Cohort
Bibliographic record
Abstract
OBJECTIVE: To analyze the trends in biologics use at a specialized center over a period of 20 years. METHODS: We performed a retrospective analysis of 571 patients diagnosed with psoriatic arthritis enrolled in the Toronto cohort who initiated biologic therapy between January 1, 2000, and July 7, 2020. The probability of drug persistence over time was estimated nonparametrically. The time to discontinuation of first and second treatment was analyzed using Cox regression models, whereas a semiparametric failure time model with a gamma frailty was used to analyze the discontinuation of treatment over successive administrations of biologic therapy. RESULTS: The highest 3-year persistence probability was observed with certolizumab when used as first biologic treatment, while interleukin-17 inhibitors had the lowest probability. However, when used as second medication, certolizumab had the lowest drug survival even when accounting for selection bias. Depression and/or anxiety were associated with a higher rate of drug discontinuation due to all causes (relative risk [RR] 1.68, P = 0.01), while having higher education was associated with lower rates (RR 0.65, P = 0.03). In the analysis accommodating multiple courses of biologics, a higher tender joint count was associated with a higher rate of discontinuation due to all causes (RR 1.02, P = 0.01). Older age at the start of first treatment was associated with a higher rate of discontinuation due to side effects (RR 1.03, P = 0.01), while obesity had a protective role (RR 0.56, P = 0.05). CONCLUSION: Persistence in taking biologics depends on whether the biologic was used as first or second treatment. Depression and anxiety, higher tender joint count, and older age lead to drug discontinuation.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".