Expediting telehealth use in clinical research studies: recommendations for overcoming barriers in North America
Bibliographic record
Abstract
Despite data supporting the rapid adoption of telehealth in the delivery of clinical care in North America, the implementation of telehealth visits in clinical research studies has faced critical barriers. These challenges include: (1) variations in state licensure requirements for telehealth; (2) disparities in access to telehealth among disadvantaged populations; (3) lack of consistency among individual Investigational Review Boards (IRBs). Each barrier prevents the systematic conversion of research protocols to include telehealth visits. The Parkinson’s Foundation and members of the Parkinson Study Group submit this Comment to highlight current challenges to implementing telehealth visits for clinical research studies. Our objective is to provide a consensus statement emphasizing the urgent need for regulators to standardize adoption of telehealth practices and to propose recommendations to reduce the burden for implementation in existing research study protocols.
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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 | 0.701 | 0.747 |
| Meta-epidemiology (narrow) | 0.003 | 0.006 |
| Meta-epidemiology (broad) | 0.006 | 0.010 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.011 | 0.021 |
| Scholarly communication | 0.027 | 0.052 |
| Open science | 0.016 | 0.028 |
| Research integrity | 0.039 | 0.045 |
| Insufficient payload (model declined to judge) | 0.006 | 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; the direct Gemma label and the distilled Codex classifier 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".