Adherence patterns in naïve and prevalent use of infliximab and its biosimilar
Bibliographic record
Abstract
INTRODUCTION: Although short-term clinical trials have demonstrated that switching from infliximab (INF) bio-originator to its biosimilar is safe with no significant loss of efficacy, there are limited real-world data comparing their patterns of use and adherence. METHODS: Using 2015-2018 IBM Marketscan data, we established 4 cohorts of patients with at least one administration or pharmacy claim for INF bio-originator or biosimilar in 2017, including INF naïve biosimilar users, INF prevalent biosimilar users, INF naïve bio-originator users, and INF prevalent bio-originator users, defined according to their prior use of INF from 2015 to their first INF administration in 2017. The proportion of days covered (PDC) was calculated for patients with at least 6, 12, or 18 months of follow-up time. Factors associated with optimal adherence (PDC > 80%) were evaluated using log-binomial models. RESULTS: We identified 96 INF naïve biosimilar users, 223 INF prevalent biosimilar users, 2,149 INF naïve bio-originator users, and 10,970 INF prevalent bio-originator users. At the end of 18 months of follow-up, 64% of INF prevalent bio-originators, 48% of INF naïve biosimilars, 41% of INF naïve bio-originators, and 36% of INF prevalent biosimilars had optimal adherence. Depression, previous hospitalization, and greater use of prior biologics were negatively associated with adherence, whereas IBD diagnoses (referent to RA) and age 55-64 (referent to < 35) were positively associated with high adherence. CONCLUSION: INF prevalent users had higher adherence in our analyses than INF naïve users. However, further studies with larger sample size are needed to evaluate INF biosimilar users' adherence.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".