A Population-Based Study of Health-Care Resource Use Among Infliximab Users
Bibliographic record
Abstract
OBJECTIVES: We sought to describe the characteristics of health-care utilization (HCU) among patients with Crohn's disease using infliximab (IFX). METHODS: Using the University of Manitoba Inflammatory Bowel Disease Epidemiology Database (UMIBDED), we extracted all subjects with newly prescribed IFX, newly prescribed purine analogs (azathioprine, AZA) (without IFX), newly prescribed steroids (Ster) (without IFX or purine analogs) after 2001, and those not prescribed any of these drugs (ND). All of the subjects must have had HCU data available for 5 years before initial prescription and for 3 years afterward. We analyzed the number of physician visits, hospital visits, and surgeries. RESULTS: IBD-associated physician visits were consistently higher for IFX, both pre- and post-initial dosing, although overall physician visits were similar between IFX, AZA, and Ster. There was a steep rise in hospitalizations in the 6 months before initial prescription of IFX, AZA, or Ster, and hospitalizations were higher in the IFX cohort until 18-24 months after the first prescription, at which point levels fell to those evident 2-5 years before initiating IFX and to levels in the other drug groups. Likelihood of surgery post-dosing was greater in IFX than in AZA or ND for up to 36 months but was not different than Ster. CONCLUSIONS: In a "step-up" approach to IFX use, it takes 2 years for the physician visits to reduce to 2-year pre-dosing rates and 18-24 months to reach hospitalization rates at 2 years pre-dosing and hospitalization rates of the AZA and Ster groups. Surgical rates to 3 years post-dosing were still higher than in AZA or ND groups.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".