Pro-Con Debate: Should All General Anesthesia Be Done Using Target-Controlled Propofol Infusion Guided by Objective Monitoring of Depth of Anesthesia?
Bibliographic record
Abstract
In this Pro-Con commentary article, we discuss whether all general anesthesia should be done using target-controlled propofol anesthesia guided by monitoring of depth of anesthesia. This is an ongoing debate since more than 25 years, representing a scientific, cultural as well as geographical divide in the anesthesia community. The Pro side argues that total intravenous anesthesia causes less postoperative nausea and higher patient satisfaction than anesthesia using volatile anesthetics. Target-controlled infusion (TCI) of anesthetic agents allows for better titration of intravenous anesthesia using pharmacokinetic models. Processed EEG monitors, such as bispectral index monitoring, allows for better assessing the effect of TCI anesthesia than solely assessment of clinical parameters, such as ECG or blood pressure. The combination of TCI propofol and objective depth of anesthesia monitoring allows creating a pharmacokinetic-pharmacodynamic profile for each patient. Finally, anesthesia using volatile anesthetics poses health risks for healthcare professionals and contributes to greenhouse effect. The Con side argues that for procedures accompanied with ischemia and reperfusion injury of an organ or tissue and for patients suffering from a severe inflammation' the use of volatile anesthetics might well have its advantages above propofol. In times of sudden shortage of drugs, volatile anesthetics can overcome the restriction in the operating theater or even on the intensive care unit, which is another advantage. Volatile anesthetics can be used for induction of anesthesia when IV access is impossible, end-tidal measurements of volatile anesthetic concentration allows confirmation that patients receive anesthetics. Taking environmental considerations into account, both propofol and volatile anesthetics bear certain harm to the environment, be it as waste product or as greenhouse gases. The authors therefore suggest to carefully considering advantages and disadvantages for each patient in its according environment. A well-balanced choice based on the available literature is recommended. The authors recommend careful consideration of advantages and disadvantages of each technique when tailoring an anesthetic to meet patient needs. Where appropriate, anesthesia providers are encouraged to account for unique features of anesthetic drug behavior, patient-reported and observed postoperative outcomes, and economic and environmental considerations when choosing any of the 2 described techniques.
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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 | 0.010 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.035 | 0.032 |
| Insufficient payload (model declined to judge) | 0.008 | 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".