Willingness To Pay To Eliminate the Risk of Restenosis Following Percutaneous Coronary Intervention
Bibliographic record
Abstract
BACKGROUND: Percutaneous coronary intervention (PCI) remains limited by the risk of restenosis. Patients' perceptions of the health benefits and value of avoiding restenosis are incompletely known. METHODS AND RESULTS: We used a contingent valuation approach to assess the willingness to pay (WTP) for a hypothetical treatment that eliminates the risk of restenosis among 270 PCI patients. Patients were provided with a scenario describing a baseline 10% or 20% probability of restenosis in the year following the procedure, which could lead to repeat PCI or, more rarely, bypass surgery, without any increase in mortality. Six different "take it or leave it" bids ($500, $1000, $1500, $2000, $2500, and $3000) and both risk levels were randomly assigned. Multiple logistic regression was used to identify independent predictors of a positive response to the WTP question. Using nonparametric methods, the median WTP to eliminate the risk of restenosis was estimated at $2802. As expected, higher income was independently associated with a higher probability of a positive response to the WTP question (odds ratio, 2.81; 95% CI, 1.32 to 5.97). Bids also were independently associated with the probability of being willing to pay, and this association followed a quadratic effect. Below $1500, bid had little impact on patient answers. However, as prices increased, the probability of being willing to pay started to decrease sharply. CONCLUSION: The potential to eliminate the risk of restenosis, a benign complication, would have substantial value for patients undergoing PCI.
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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".