Estimate of the cost per responder for treatment with biological therapies of moderate-to-severe plaque psoriasis in Colombia for first-year and maintenance periods
Bibliographic record
Abstract
INTRODUCTION: Psoriasis is a chronic systemic inflammatory disease manifesting as erythematous and desquamative dermatoses. OBJECTIVES: This study estimated the cost per responder (CPR) for the treatment of moderate-to-severe plaque psoriasis with biologic therapies approved by the Colombian regulatory agency. METHODS: This secondary study used a modeling based CPR estimation to evaluate psoriasis therapies in Colombia. We calculated CPR of achieving Psoriasis Area and Severity Index (PASI) scores of 75, 90, and 100 for biological treatments based on the number needed to treat (NNT), reported in previously published network meta-analyses. We calculated CPR for the first year and for the maintenance period. We ranked alternatives using the estimated CPR from each literature source using the Borda count method. RESULTS: Adalimumab, infliximab and etanercept were the least expensive alternatives. Ixekizumab, guselkumab and secukinumab were the treatments with the lowest NNT for PASI 75, 90, and 100. For both first year and maintenance periods, adalimumab, infliximab, guselkumab and ixekizumab had the lowest CPR. Sensitivity analyzes showed consistent results. CONCLUSIONS: The application of CPR analysis of biologics to treat plaque psoriasis demonstrated that adalimumab, infliximab, guselkumab, and ixekizumab had the lowest CPR in the first year of treatment and during the maintenance period.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".