Bispectral Index Values, Sedation-Agitation Scores, and Plasma Lorazepam Concentrations in Critically Ill Surgical Patients
Bibliographic record
Abstract
BACKGROUND: Publications on the use of the bispectral index for sedation monitoring in the intensive care unit are increasing. However, few studies have involved correlation of bispectral index with serum drug concentrations. OBJECTIVES: To assess the degree of correlation between bispectral index values, scores on the Sedation-Agitation Scale, and steady-state serum concentrations of lorazepam. METHODS: A prospective open-label study of patients in a surgical intensive care unit who were receiving mechanical ventilation and continuous infusions of lorazepam for more than 24 hours. Bispectral index was measured (BIS-XP, Aspect Medical, Norwood, Massachusetts) to assess patients' sedation. Sensors were applied and values recorded before and after stimulation (endotracheal suctioning). Concomitant plasma samples were obtained to measure lorazepam concentration and scores on the Sedation-Agitation Scale were recorded. RESULTS: Sixteen patients were studied. Correlations between plasma concentrations of lorazepam and measurements of bispectral index for 1, 2, and 3 minutes before endotracheal stimulation were poor (0.21, 0.29, and 0.25, respectively). Correlation of peak values for bispectral index (after stimulation) with plasma concentrations of lorazepam was 0.29. Correlations of scores on the Sedation-Agitation Scale with the aforementioned values for bispectral index were similarly poor. Area under the curve for bispectral index values also correlated poorly with plasma concentration of lorazepam (0.19) and score on the Sedation-Agitation Scale (0.10). CONCLUSIONS: The correlation between bispectral index and score on the Sedation-Agitation Scale was poor. Correlation between bispectral index and plasma concentration of lorazepam was modestly better, but insufficient for clinical utility.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".