Legal and ethical considerations in medical records management within emergency cardiology
Bibliographic record
Abstract
Background _ Telemedicine is a promising healthcare solution, particularly in underserved areas. It is cost-effective and accessible in both developed and developing countries. However, it faces challenges such as misdiagnosis, inconsistent legal regulations, and potential liability. The cost of telemedicine services is similar to in-person encounters, and pay equality is not guaranteed. Ethical and legal obstacles must be considered when implementing telemedicine programs, such as obtaining informed consent and understanding privacy risks. Aim of Work – Our aim was to emphasize the present state and identify the remaining requirements for implementing ethical and legal norms in telemedicine. Discrepancies have arisen among existing laws, lawmakers, service providers, various medical services, and, most significantly, the patient's connection with their data and the use of that data. Methods – The study conducted a comprehensive search of English literature published between 2010 and 2018 on PubMed, Scopus, and Web of Science. The search utilized specific keywords such as "Telemedicine," "Ethics," "Malpractice," "Telemedicine and Ethics," "Telemedicine and Informed consent," and "Telemedicine and Malpractice." Various types of articles, including research articles, review articles, and qualitative studies, were examined and analyzed. The abstracts were assessed based on the selection criteria, using the Newcastle-Ottawa Scale criteria.
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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 | 0.182 | 0.402 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.008 | 0.021 |
| Scholarly communication | 0.016 | 0.019 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.011 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".