On-line Monitoring of End-tidal Propofol Concentration in Anesthetized Patients
Bibliographic record
Abstract
BACKGROUND: Propofol (2,6-diisopropylphenol) has some volatility, so it can be detected in expired breath of individuals receiving intravenous propofol. This study measured volatile propofol exhaled by patients and investigated the relation between exhaled and plasma propofol concentrations. METHODS: Nineteen patients with American Society of Anesthesiologists physical status I or II who were undergoing elective surgery participated in this two-part study. In study 1 (n = 11), anesthesia was induced with 2 mg/kg propofol, 0.1 mg/kg vecuronium, and 2 microg/kg fentanyl. After intubation, propofol was administered continuously for 60 min at each of three rates: 3, 6, and 9 mg x kg(-1) x h(-1). Blood samples were obtained just before each change in the infusion rate, and the plasma concentrations of propofol were measured. The exhaled propofol concentration was measured continuously by means of proton transfer mass spectrometry. End-tidal propofol concentrations during blood sampling were averaged and compared with plasma propofol concentrations. In study 2 (n = 8), after induction of anesthesia, patients received a bolus injection of 2 mg/kg propofol, and the exhaled propofol concentration was measured. RESULTS: Volatile propofol was detected in expired gas from all study patients. From study 1, the authors obtained 24 paired data points, i.e., concentrations of end-tidal and plasma propofol. With Bland-Altman analysis, bias +/- precision was 5.2 +/- 10.4 with 95% limits of agreement of -15.1 and 25.6. In study 2, the exhaled propofol concentration curve showed an obvious peak in all patients. CONCLUSIONS: Agreement between plasma and exhaled propofol concentrations suggests that proton transfer mass spectrometry can be used for real-time propofol monitoring.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".