494 Expert group decision making for pharmacogenomic testing in Ontario
Bibliographic record
Abstract
OBJECTIVES/GOALS: There is a need to better understand how governments develop strategies to adopt, evaluate, and implement novel health technologies in a public healthcare system. The goal of this project is to understand this strategy development process for the translation of pharmacogenomic (PGx) testing in Ontario, Canada. METHODS/STUDY POPULATION: This observational case study of the Ontario Health PGx Working Group focused on developing recommendations for a PGx testing implementation strategy in the province. The group included 9 individuals affiliated with Ontario Health and 13 healthcare experts from multiple clinical fields. Ontario Health is the government agency that oversees provincial healthcare planning and service delivery. Guided by the Translational Thinking Framework and qualitative research methods, we observed the working group’s activities for eight months. We collected meeting recordings, slideshow decks, emails, and group characteristics. We used descriptive statistics and a nine-step inductive approach to analyze the data to create process maps, a case report, and key decision summaries. RESULTS/ANTICIPATED RESULTS: There were 19 meetings conducted remotely with videoconferencing technology. Throughout the working group’s activities, we identified 15 key decisions related to either administrative processes or PGx scientific content. We further stratified these two categories into four main themes relating to decisions about 1) membership involvement, 2) logistical management, 3) discussion and recommendation scope, and 4) information dissemination. These four decision themes represent tools by which Ontario Health guided the expert group activities and achieved their goal of generating a strategic roadmap for PGx testing implementation in Ontario. DISCUSSION/SIGNIFICANCE: The Ontario government makes decisions about how expert groups function by monitoring and controlling the group’s activities to ensure efficiency, standardization, and practicality. Describing expert group decision-making increases transparency and highlights the critical role they play in the translational pathway of health technologies.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.010 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".