<p>Stated Preferences for Attributes of a <em>CYP2C19</em> Pharmacogenetic Test Among the General Population Presented with a Hypothetical Acute Coronary Syndrome Scenario</p>
Bibliographic record
Abstract
Background: Pharmacogenetic (PGx) testing identifies pharmacotherapeutic risks to permit personalized therapy. Identifying the genetic profile of patients with acute coronary syndrome (ACS) who are considered for therapy with clopidogrel (P2Y 12 receptor blockers) and acetylsalicylic acid (ASA) contributes to the treatment paradigm. Patient preferences would inform a collaborative framework and by extension inform healthcare policy formulation. Purpose: To quantify stated preferences (willingness to pay) for attributes of a novel point-of-care PGx ( CYP2C19 ) test using a discrete choice experiment (DCE) from the general public in Ontario, Canada, and to identify starting point bias of the cost attribute. Methods: A web survey was created and included a questionnaire, decision board, and a DCE. DCE choice sets include the following attributes (levels): sample collection (blood, finger prick, and cheek swab), turnaround time for results (1 hr, 3 days, and 1 week), and cost in additional insurance premiums. The presence of starting point bias (cost attribute levels of $0, $1, $5 or $0, $2, $10) in the estimation of willingness to pay (WTP) was tested. Results: Estimates for turnaround time and cost attributes were statistically significant. Coefficients related to the starting point bias were also significant. Approximately 67% of survey participants chose the PGx test compared to status quo treatment options. WTP for a 1 hr turnaround time compared to a 1-week turnaround time was $10.77 (95% CI 9.58 -12.25). Conclusion: This translational study shows preference for a point of care PGx test. Keywords: discrete choice experiment, pharmacogenetic test, patient preference, starting point bias
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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".