Bibliographic record
Abstract
Despite advancements in anesthesia techniques and equipment, anesthesia-related complications continue to occur, often due to human errors and the limitations of current tools. The integration of artificial intelligence (AI) into anesthesia presents a transformative opportunity to enhance patient safety and improve outcomes throughout the perioperative journey. By leveraging machine learning (ML) and deep learning (DL), AI can analyze vast datasets to detect subtle patterns, predict risks such as difficult intubation or hemodynamic instability, and enable more proactive management. Furthermore, AI-driven systems have the potential to optimize anesthetic control, reducing variability and enhancing precision. In the postoperative phase, AI can improve personalized pain management and monitoring, further enhancing recovery and patient satisfaction. However, challenges such as data privacy concerns, lack of opacity, and the potential erosion of human interaction in care must be carefully addressed. Ultimately, the future of anesthesiology lies in a synergistic relationship between AI and human expertise – where AI amplifies precision and foresight, while anesthesiologists maintain the empathy and clinical judgment needed to navigate complex patient care.
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 | 0.037 | 0.171 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.016 | 0.034 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.009 | 0.013 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".