Bibliographic record
Abstract
PURPOSE OF REVIEW: Robots are present in surgery, to a much lesser extent in the field of anesthesia. The purpose of this review is to show the latest and most important findings in robotic anesthesia. Moreover, this review argues the importance and utility of robots in anesthesia. RECENT FINDINGS: Over the years, many closed-loop systems have been developed; they were able to control only one or two of the three components of anesthesia: hypnosis, analgesia, or muscle relaxation. McSleepy controls all three components of anesthesia, from induction to emergence of anesthesia. Telemedical applications have not only led to remote monitoring but even to remotely controlled anesthesia, such as transcontinental anesthesia. A new closed-loop system for sedation, called Sedasys, could revolutionize the field of nonoperating room sedation. 'Manual robots' are used to help and replace anesthesiologists performing anesthesia procedures. Specific robots for intubation and nerve blocks have been developed and tested in humans. SUMMARY: Robots can improve performance in anesthesia and healthcare. Closed-loop systems are the basis for pharmacological robots. Safe anesthetic care might be delivered through teleanesthesia whenever qualified personnel are not available or need support. Mechanical robots are being developed for anesthesia 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.013 |
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".