Impact of switching to infliximab biosimilars on treatment patterns among US veterans receiving innovator infliximab
Bibliographic record
Abstract
OBJECTIVE: To compare treatment patterns of United States (US) veterans stable on innovator infliximab (IFX) who switched to an IFX biosimilar (switchers) or remained on innovator IFX (continuers). METHODS: US Veterans Healthcare Administration data (01/2012-12/2019) were used to identify adults with rheumatoid arthritis (RA), psoriatic arthritis (PsA), plaque psoriasis (PsO), ankylosing spondylitis (AS), or Crohn's disease and ulcerative colitis (i.e. inflammatory bowel disease [IBD]), treated with innovator or biosimilar IFX. Index date was the first IFX biosimilar administration for switchers or a random innovator IFX administration for continuers. Patients were required to have ≥5 innovator IFX administrations during the 12 months pre-index (prevalent population). Patients with ≥12 months of observation prior to the first innovator IFX administration were analyzed as the primary population (incident population), and data were assessed from start of innovator IFX. Inverse probability of treatment weighting was used to balance baseline characteristics between cohorts. Treatment patterns were evaluated post-index; continuers were censored before switching to IFX biosimilar. Discontinuation was defined as switching to another biologic (including innovator IFX) or having ≥120 days between 2 consecutive index treatment records. RESULTS: < .001). Of 653 switchers switching to another innovator biologic, 594 (91.0%) switched back to innovator IFX. Results were similar among the prevalent population and RA and IBD subgroups. CONCLUSION: Patients switching from innovator to biosimilar IFX were more likely to discontinue treatment and switch to another innovator biologic (notably back to innovator IFX) than those remaining on innovator IFX; however, reasons for discontinuation and switching are unknown.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 0.000 |
| 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".