Comparative Health-Care Cost Advantage of Ipratropium over Tiotropium in COPD Patients
Bibliographic record
Abstract
Objective: To compare the total direct health-care costs of patients treated with tiotropium and ipratropium. Methods: We conducted a cohort study of health-care costs in British Columbia, Canada, by comparing new patients on tiotropium with new patients on ipratropium. Direct health-care costs for study patients were measured in the first 2 years after initiating inhaled anticholinergic treatment. Differences in direct health-care costs between tiotropium and ipratropium patients were estimated by using quantile regression. We analyzed cost differences in the 10th percentile, median, and 90th percentile of patients by cost. High-dimensional propensity score analysis was used as a method of adjustment for potential confounding factors. Results: The study population had 3,140 tiotropium patients and 26,182 ipratropium patients. Higher health system costs in patients who started on tiotropium instead of ipratropium were observed in patients in the median and 10th percentile. The magnitude of these increases was comparable to the price difference between the two drugs. Health system costs in the 90th percentile were not significantly different between tiotropium and ipratropium patients. Conclusions: The results of this study did not support the preferential use of tiotropium over ipratropium as a basis for savings in direct health-care costs.
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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) | 0.001 | 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".