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Ella Boone

Alumni
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A longitudinal study of research productivity in McGill University's MD-PhD and Clinician Investigator Program Graduates from 2000 to 2017
Jamie Magrill
Jean-Pierre Falet
Ella Boone
Mimosa Luigi
Isabella Arthur
Paria Asadi
Anna Frumkin
Mark Sorin
Joan Miguel Romero
Julia Luo
Nathalie Johnson
Mark J. Eisenberg
BACKGROUND: Canadian MD-PhD and Clinician Investigator Program (CIP) pathways both train physician-scientists, yet longitudinal data on grad… (see more)uate research productivity are limited. METHODS: We conducted a longitudinal study of McGill University MD-PhD and CIP graduates who completed these programs between 2000 and 2017, using sex- and year-matched MD-only graduates as controls. Outcomes were collected for publications indexed through to December 31, 2022, and included H-index, total publications, total citations, per-paper journal impact factor, per-paper citations, and authorship position (first, second, senior). Productivity was assessed across four time periods: (1) pre-medical/medical training, (2) residency, (3) post-residency/fellowship, and (4) independent practice. RESULTS: Among 549 graduates (MD-PhD = 31; CIP = 97; MD-only = 421), research productivity, research impact, and active research involvement (defined as ≥3 first- or senior-author papers in the prior 5 years) were similar between MD-PhD and CIP graduates; both exceeded MD-only graduates across all metrics. Within-group sex differences were not significant. MD-PhD graduates were more productive during pre-medical/medical training, whereas CIP graduates were comparatively more productive during residency. Sustained productivity in independent practice correlated with research engagement during medical school, residency, and fellowship. In both programs, graduates with active research involvement had higher authorship counts in the 10 years immediately following graduation from medical school. DISCUSSION: Research productivity and impact were comparable between MD-PhD and CIP graduates, and both groups exceeded MD-only peers across measured research metrics. Across both programs, graduates with active research involvement had higher early-career authorship counts, particularly during residency and post-residency/fellowship time periods. These findings provide descriptive benchmarking data for future studies of physician-scientist training pathways in Canada.
2026-06-30
Clinical and Investigative Medicine (published)
doi.org
Synthetic Validation of Pediatric Trust Instruments using Persona-Driven Large Language Models
Ella Boone
Katya Loban
Elena Guadagno
Dan Poenaru
Trust is foundational to patient-physician relationships and is associated with improved care-seeking and adherence in primary care. However… (see more), validated trust instruments for pediatric emergency and surgical contexts are lacking, and traditional instrument development is slow and resource-intensive. Large language models (LLMs) could streamline the validation process by serving as scalable, systematic expert panel surrogates. We developed four new trust assessment instruments: two for patient-families and two for physicians. Two-phase content validation was conducted using two parallel synthetic and human expert panels. Synthetic panels consisted of three persona-prompted LLMs (Claude Sonnet 4, GPT-5, Grok4). Human panels served as traditional comparators. Scale-Content Validity Index (S-CVI) and Fleiss’ kappa (k) acceptance thresholds were set at ≥0.80. Combined human–synthetic expert panels revealed substantial inter-rater reliability across all instruments. Fleiss’ kvalues for dimensional validation were: patient-family = 0.84 (95% CI [0.72, 0.96]), physician = 0.87 (95% CI [0.72, 1.00]);contextual validation: patient-family = 0.83 (95% CI [0.73, 0.93]), physician = 0.88 (95% CI [0.80, 0.96]). All instruments exceeded S-CVI ≥0.80 thresholds across both validation phases. Persona-prompted LLMs demonstrated comparable validity outcomes to human experts while accelerating validation timelines from months to weeks. Future research needs to evaluate this approach across psychometric testing phases. This synthetic instrument validation methodology offers a scalable blueprint for healthcare measurement development, enabling faster creation of validated tools to support evidence-based patient care.
2025-11-26
MedRxiv (preprint)
doi.org