A cost effectiveness analysis of urate lowering drugs in nontophaceous recurrent gouty arthritis
Bibliographic record
Abstract
Objective, To determine in a Canadian health care setting the cost effectiveness of urate lowering drugs (ULD) in the treatment of nontophaceous gouty arthritis with recurrent attacks and to evaluate the least costly regimen among available ULD.Methods, A decision analysis model was designed using hypothetical cohorts of patients who present 1 to 4 recurrent attacks/year. It incorporated costs and probabilities estimated from the published literature. Effectiveness was defined as the number of recurrent attacks averted by each treatment strategy (ULD or No ULD), The incremental cost effectiveness ratio was defined as the ratio of the additional cost incurred by a management strategy compared with the additional benefit derived from it. A multiway sensitivity analysis was built to allow the modelling of extreme case scenarios favoring (best ULD scenario) and disfavoring (worst ULD scenario) the ULD therapy.Results. Using the baseline scenario estimates for the hypothetical cohort of patients presenting one attack/year, the total annualized costs per patient associated with ULD and No ULD treatment were Cdn $426.27 and 267.27, respectively. The average cost effectiveness ratios were $592.25 and 5,345.37, respectively, per attack averted, For this cohort of patients the incremental cost effectiveness ratio ranged from $99.59 (best ULD scenario) to 489.26 (worst ULD scenario). The treatment with ULD is cost saving if patients present 2 or more attacks/year. Allopurinol in its generic formulation was the ULD that presented the lowest incremental cost effectiveness ratio.Conclusion, ULD treatment is cost effective. It is also cost saving if patients present 2 or more recurrent attacks/year.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".