South African and international legislature with relevance to the application of electronic documentation in medicolegal autopsies for practice and research purposes
Bibliographic record
Abstract
Abstract Background Forensic and legal medicine requires all documentation to be recorded in a manner that is admissible in court. Issues surrounding privacy, confidentiality, and security mar the implementation of electronic document systems in medicine. Awareness of current legislature governing record keeping and electronic documentation especially in modern medicine and forensic medicine has not been sufficiently explored. This study explored the current South African and international laws that govern admissibility of evidence, especially relating to electronic evidence, for use in court and research, Findings Egypt, UK, Canada and the USA have similar legislation to South Africa regarding admissibility of electronic records. The South African Electronic Communications and Transactions Act no. 25 of 2002 defines data and the Criminal Procedure Act 51 of 1977 further defines the admissibility of evidence in court and the National Health Act regulates publication of deceased information after death. Conclusions Forensic medicine requires all documentation to be admissible in court and the storage of data thus requires proper custodianship and a high level of security, which can be achieved with modern technology. Modern medicine is evolving and technology can create secure and efficient methods of record keeping which will benefit forensic and legal medicine. Knowledge of the laws regarding admissibility of evidence can assist in creating electronic evidence that is permitted in court and can be used for research.
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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 | 0.019 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".