Digital Health Care, Telemedicine, and Medicolegal Issues in Orthopedics: A Review
Bibliographic record
Abstract
The use of technologies in medicine has great potential to reduce the costs of health care services by making appropriate decisions that provide timely patient care. The evolution of telemedicine poses a series of clinical and medicolegal considerations. However, only a few articles have dealt with telemedicine and orthopedics. This review assesses the ethical and medicolegal issues related to tele-orthopedics. A systematic review was performed including papers published between 2017 and 2021 focusing on the main medicolegal and clinical-governance aspects of tele-orthopedics. Most of the articles were published during the COVID-19 pandemic, confirming the impetus that the pandemic has also given to the spread of telemedicine in the orthopedic field. The areas of interest dealt with in the scientific evidence, almost exclusively produced in the USA, Europe, the UK, and Canada, are quality, patient satisfaction, and safety. The impact of telemedicine in orthopedics has not yet been fully evaluated and studied in terms of the potential medicolegal concerns. Most of the authors performed qualitative studies with poor consistency. Authorizations and accreditations, protection of patient confidentiality, and professional responsibility are issues that will certainly soon emerge.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".