Online Therapeutic Portals for Sharing Health Research: Comparative Guidance amid Regulatory Uncertainty
Bibliographic record
Abstract
Online resources offer a uniquely efficient way of sharing health research with scientists and the public. Using web portals to make results and study information available to diverse audiences could work to accelerate research translation and empower patients to play a more active role in their care. But using online tools to broadly share health information raises several challenging ethical and regulatory questions. Issues such as equity, privacy, and patient empowerment may create challenges for regulators, portal developers, as well as researchers. It is additionally unclear whether web portals designed to facilitate access to research results and general health information will be regulated as medical devices under emerging regimes that control software with medical purposes. This paper aims to comparatively address whether online therapeutic portals for sharing health research are likely to be regulated in Canada, the United States, the United Kingdom, and France. We find that though these jurisdictions have each taken recent steps to regulate software as medical devices, the applicable regimes will generally not capture online portals for sharing health research. Though online portals for sharing health research are probably unregulated in many (if not most) jurisdictions, agencies have nevertheless signalled their concerns regarding several important ethical considerations (such as equity, transparency, and safety), to which portal developers and researchers should be attentive and respond. We describe here one set of issues highlighted by regulators – that is, efficiency, equity, transparency, confidentiality, communication, empowerment, training, and safety & efficacy – and consider how to best guide the design of online portals in a context of regulatory uncertainty.
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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 | 0.342 | 0.536 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.010 | 0.017 |
| Scholarly communication | 0.028 | 0.035 |
| Open science | 0.007 | 0.016 |
| Research integrity | 0.012 | 0.010 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".