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Record W4393869420 · doi:10.1017/cts.2024.420

494 Expert group decision making for pharmacogenomic testing in Ontario

2024· article· en· W4393869420 on OpenAlexaffabout
Samuel Neumark, Mary Schmitz, Ayeshah G. Mohiuddin, D. C. Gillespie, Zubin Austin, Richard Foty, Joseph Ferenbok

Bibliographic record

VenueJournal of Clinical and Translational Science · 2024
Typearticle
Languageen
FieldMedicine
TopicEthics in Clinical Research
Canadian institutionsCentre for Addiction and Mental HealthUniversity of Toronto
Fundersnot available
KeywordsPharmacogenomicsGroup (periodic table)Group decision-makingComputer sciencePsychologyMedicinePharmacologySocial psychologyChemistry

Abstract

fetched live from OpenAlex

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.051
metaresearch head score (Gemma)0.065
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.307
Threshold uncertainty score0.618

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0510.065
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0200.009
Scholarly communication0.0060.003
Open science0.0030.008
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0060.000

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.

Opus teacher head0.686
GPT teacher head0.663
Teacher spread0.023 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes2
Has abstractyes

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