Pharmacogenomics guided versus standard antidepressant treatment in a community pharmacy setting: A randomized controlled trial
Bibliographic record
Abstract
The literature on pharmacogenomics as a tool to support antidepressant precision is burgeoning. Recently, a more active role has been argued for pharmacists in pharmacogenomic testing, with both pharmacists and family physicians perceiving pharmacist-led testing as a valuable method by which to scale this innovation for depression treatment. In this prospective, single-blind randomized controlled design, we evaluated the impact of pharmacogenomics guided versus standard antidepressant treatment of depression and anxiety, implemented in three large community pharmacies. Participants were 213 outpatients diagnosed with major depressive disorder and/or generalized anxiety disorder, randomized to receive pharmacogenomics guided (n = 105) or standard antidepressant treatment (n = 108); participants were blinded to the study. Patient reported outcomes of depression, anxiety, disability, and treatment satisfaction were assessed at months 0, 1, 3, and 6. Hypotheses were investigated using mixed effect models on the full data. All clinical outcomes improved significantly. The primary outcome (depression) and two secondary outcomes (generalized anxiety and disability) exhibited significant time by group interactions indicating that they improved for participants who received pharmacogenomics guided treatment more so than they did for participants who received standard treatment. Treatment satisfaction improved similarly for both groups. Results contribute to a growing body of work evaluating the impact of pharmacogenomics testing to inform antidepressant medication treatment for depression and anxiety, and provides important initial evidence for the role of pharmacists in care delivery. Pharmacogenomic testing may be a valuable tool to allow pharmacists to more effectively collaborate in facilitating clinical treatment decisions. ClinicalTrials.gov registration: (NCT03591224).
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".