Legal Implications for Actions Considered Iatrogenic: Knowledge and Practices of the Surgical Team, Valledupar, Cesar/Colombia
Bibliographic record
Abstract
Introduction: The errors considered iatrogenic are caused by different causes that are mostly considered foreseeable, including inaccuracy, recklessness and ignorance, all these actions with ethical-legal repercussions. In Colombia, despite not having studies that position iatrogeny as one of the main causes of death. It is estimated that in Colombia 180,000 people die each year for this reason. Iatrogeny is described in the USA as the third leading cause of death. Objective: To evaluate the knowledge and aspects related to negligence, inexperience, recklessness, that the surgical team has in regard to its ethical-legal responsibilities. Methodology: Descriptive cross-sectional study. A total of 93 professionals were surveyed who are part of the surgical team of two health-care institutions in the department of Cesar, Colombia during 2019. Results: Surveyed professionals showed a 100% ignorance about the process to follow during an adverse event. It was identified that 32 % of the professionals and technicians surveyed do not have insurance to back them up legally. In addition, 31% said that errors such as negligence, recklessness and inexperience can occur in parallel with criminal and civil repercussions. The surgical team considers that the most frequent error are elements and instruments left in the cavity (oblites) in 64%. Conclusions: For surgical personnel, negligence is the error with the greatest penal repercussion if it is related to the death of the patient, surgery on the wrong patient and wrong side of the body, in addition to oblites or foreign bodies left in the cavity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".