Considerations for the implementation of pharmacogenomic (PGx) testing to guide antidepressant prescribing in primary care in Ontario, Canada
Bibliographic record
Abstract
Background: Major depressive disorder (MDD) is commonly treated with antidepressants, but many patients undergo trial-and-error to find the medication best suited for them. Pharmacogenomic (PGx) testing was developed to provide prescribing guidance for a variety of medications including antidepressants. PGx testing is not yet part of standard depression care in Canada, but clinical implementation efforts are ongoing. Patient perspectives, as well as the views of people with professional expertise about this technology, are critical to health technology assessment (HTA) and policy decisions, and can inform implementation. Methods: This dissertation produces actionable patient and key informant (i.e., clinician, scientists, policy actor, industry member) perspectives evidence for future HTA and policy- making through three independent studies: a systematic review and qualitative meta-synthesis of patient experiences of treatment-resistant depression (TRD), and two qualitative description studies about patient and key informant perspectives on PGx testing to guide antidepressant prescribing. Results: Trial-and-error of medications often leaves patients feeling hopeless but desperate to get better. Patients and key informants both felt the main benefit of PGx testing is its potential to reduce the time between diagnosis and successful treatment. The main findings of this research were concrete suggestions for how PGx testing may be integrated in the health system. Participants preferred this technology be deployed in primary care, with patients with TRD identified as a possible priority population. The ideal PGx test would incorporate genetic variants common in non-white populations to ensure equity of health outcomes, and mechanisms to facilitate equity of access (eg., public funding) were considered important. Conclusions: Patients and key informants both described benefits of PGx testing to guide antidepressant prescribing. Patients typically raised more concerns than key informants. Future research is needed around some of the ethical and social issues related to PGx testing, as well as to address feasibility questions.
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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 | 0.026 | 0.070 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".