Assessing informed consent in medical malpractice cases associated with different surgical fields referring to Tehran's Commission of Forensic Medicine, 2017
Bibliographic record
Abstract
Background: Increasing the number of complaints against medical staff emphasizes the need for physicians to be more familiar with legal issues before and during providing medical services to the patient. Signing the informed consent form before medical practices and informing the patient of all possible outcomes can cause mental health and better collaboration of patients as well as increase the physician's self-confidence to provide better services. The current study aimed at determining the status of standard informed consent in medical cases related to different surgical fields referring to Tehran's Commission of Forensic Medicine during the first quarter of 2017. Materials and Methods: In the current descriptive, cross-sectional study, the cases of medical malpractice related to different surgical fields referring to Tehran's Commission of Forensic Medicine in the first quarter of 2017 were investigated. Data were analyzed with SPSS version 16. Results: In the current study, 124 cases of complaints against the medical staff of the surgical fields were examined. Based on the obtained data, the age and specialty of physicians, faculty status, and type of treatment center were effective in obtaining standard informed consent, and the highest percentage of allegations against the charge was related to cases attempted to obtain informed consent. Conclusion: Obtaining the standard consent can significantly improve the patient-physician relationships and reduce the rate of medical malpractice complaints.
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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 | 0.010 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".