Bibliographic record
Abstract
PURPOSE OF REVIEW: Anesthesiologists are overloaded with information and multitasking necessities in an extremely complex work environment. The purpose of this review is to present recent developments toward automated anesthesia and present future technologies for everyday clinical practice. RECENT FINDINGS: Decision support systems integrate different parameters, clinical scenarios and assessments by (non)-trained personnel into algorithms, which lead to diagnostic suggestions, triage evaluations or treatment options. Target-controlled anesthesia infusion systems reduce the anesthesiologist's workload; target-controlled analgesia systems have the potential to provide more stable hemodynamic control. Closed-loop delivery of anesthesia is feasible and provides anesthetic control as good as or better than human delivery. Teleanesthesia offers the possibility of distant preoperative assessment of the patient's fitness for anesthesia, aid of trained personnel to perform anesthetic tasks and the control of anesthesia delivery in a distant location. SUMMARY: Decision support systems help to make reliable and standardized decisions in complex environments. Target-controlled infusion systems reduce the anesthetic workload. Closed-loop systems will automate anesthesia care in the near future. Teleanesthesia offers the opportunity to provide safe anesthetic care whenever trained personnel are not available or need support.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.008 |
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".