The Research Visit of the Future: an academic-industry model for telehealth, electronic clinical outcomes assessments, and Decentralized Clinical Trials (DCTs)
Bibliographic record
Abstract
The COVID pandemic initially led to the unprecedented suspension of large numbers of clinical research studies and trials that required in-person study visits, highlighting the need to reevaluate how human participant research projects are conducted. During this time the University of Pittsburgh Clinical and Translational Science Institute (Pitt CTSI) embarked on a multi-year initiative to envision the Research Visit of the Future, which ultimately led to an academia-industry partnership that enables the scalable self-service model using a virtual clinical research platform for clinical studies at a major academic research institution. This model enables the flexible deployment of eConsent, Telehealth (audio/video) calls, synchronous and asynchronous data capture, integrated sensors and wearables, and patient engagement tools, all within a single participant-focused research platform. Academic investigators can configure their studies without custom coding across a wide range of study designs, including hybrid- and fully decentralized clinical trials. Here we present the journey to identifying the technology requirements to enrich data collection, structure of our partnership model, and considerations for crossing the digital divide to enable broader access to clinical research among diverse and under-represented communities.
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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 | 0.092 | 0.104 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.025 |
| Scholarly communication | 0.020 | 0.029 |
| Open science | 0.005 | 0.016 |
| Research integrity | 0.042 | 0.048 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".