Cost per remission and cost per response with infliximab, adalimumab, and golimumab for the treatment of moderately-to-severely active ulcerative colitis
Bibliographic record
Abstract
OBJECTIVE: To determine the short-term costs per sustained remission and sustained response of three tumor necrosis factor inhibitors (infliximab, adalimumab, and golimumab) in comparison to conventional therapy for the treatment of moderately-to-severely active ulcerative colitis. METHODS: A probabilistic Markov model was developed. This included an 8-week induction period, and 22 subsequent 2-week cycles (up to 1 year). The model included three disease states: remission, response, and relapse. Costs were from a Canadian public payer perspective. Estimates for the additional cost per 1 year of sustained remission and sustained response were obtained. RESULTS: Golimumab 100 mg provided the lowest cost per additional remission ($935) and cost per additional response ($701) compared with conventional therapy. Golimumab 50 mg yielded slightly higher costs than golimumab 100 mg. Infliximab was associated with the largest additional number of estimated remissions and responses, but also higher cost at $1975 per remission and $1311 per response. Adalimumab was associated with the largest cost per remission ($7430) and cost per response ($2361). The cost per additional remission and cost per additional response associated with infliximab vs golimumab 100 mg was $14,659 and $4753, respectively. CONCLUSIONS: The results suggest that the additional cost of 1 full year of remission and response are lowest with golimumab 100 mg, followed by golimumab 50 mg. Although infliximab has the highest efficacy, it did not exhibit the lowest cost per additional remission or response. Adalimumab produced the highest cost per additional remission and response.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".