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.
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 | 0.180 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.004 | 0.013 |
| 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; both teacher heads agree on what is shown here.
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".