Characterizing models for delivery of pharmacogenomic testing: a scoping review
Bibliographic record
Abstract
Pharmacogenomic (PGx) testing can help guide medication prescribing for a wide range of health indications. The objective of this scoping review was to understand how PGx testing has been clinically implemented and learn from these experiences. Research questions guiding this work were: (1) what different models for delivery of PGx testing have been employed? (2) what are the characteristics of each delivery model? and (3) what are the reported facilitators and barriers associated with each delivery model? A total of 134 articles reported on 125 PGx initiatives spanning 19 countries. Four unique delivery models were identified: sole prescriber-led (n = 45), prescriber-led within an interdisciplinary care team (n = 34), community pharmacist-led (n = 16), and PGx consultation service (n = 30). The unique combination of characteristics, and reported facilitators and barriers yielded distinct strengths and challenges for each identified delivery model. Findings from this review can help inform future implementation planning or expansion of PGx initiatives by presenting different delivery models that may be employed and the corresponding considerations for each approach. This information can help inform future implementers in the selection of one or more approaches that may be most suitable based on their unique contextual needs, and available infrastructures or resources.
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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 | 0.033 | 0.104 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.016 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".