Bibliographic record
Abstract
PURPOSE OF REVIEW: This review will examine research ethics in the context of globalization of clinical trials and recent rapid developments in bioscience. It will focus on international ethical guidelines and the functions of research ethics review boards in research governance. Consent issues in genetic research, which must comply with privacy laws by protecting confidentiality and privacy of personal health data, will be discussed. RECENT FINDINGS: There has been a rapid expansion of genomic and proteonomic research and biotechnology in the last decade. International ethical guidelines have been updated and the bioscience industry has developed ethics policies. At the same time, problems in academic anesthesia in the US and UK have been identified, leading to recommendations to train physician-scientists in anesthesia to stimulate research activity in the future. Anesthesiologists are joining interdisciplinary research teams and the concept of evidence-based translational research is emerging. SUMMARY: Anesthesiologists are moving towards participation in interdisciplinary research teams. They are well placed to speed the translation of research discovery into clinical practice and provide evidence-based perioperative care. This review provides the ethical framework that anesthesiologists will need to meet the challenges of this changing pattern of practice.
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 | 0.071 | 0.138 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.025 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".