Bibliographic record
Abstract
More than ever before, information and communication technologies are playing an important role in the provision of health care services. Since the onset of the COVID-19 pandemic, the use of telehealth modalities in health care has significantly increased. As a form of telehealth, remote patient monitoring (RPM) uses information technologies and telecommunication tools to collect health data from patients outside of traditional health care institutional settings and transmit the data to health care providers for monitoring and evaluation purposes. While there are many benefits to the clinical use of RPM, there are many challenges to its greater implementation in health care. One such challenge is the uncertainty regarding the liability for physicians who use RPM with their patients. As is often the case when novel technologies are introduced into health care, uncertain medical liability may have a chilling effect on the greater clinical use of RPM. To date, medical liability issues regarding RPM have not been addressed by courts and there is a paucity of literature on the topic. Our thesis aims to elucidate this uncertainty by examining how medical liability rules may apply to RPM, under both the Anglo-Canadian common law and Quebec civil law traditions
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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 | 0.009 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.014 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".