Increased Risk of Intraoperative Awareness in Patients with a History of Awareness
Bibliographic record
Abstract
BACKGROUND: Patients with a history of intraoperative awareness with explicit recall (AWR) are hypothesized to be at higher risk for AWR than the general surgical population. In this study, the authors assessed whether patients with a history of AWR (1) are actually at higher risk for AWR; (2) receive different anesthetic management; and (3) are relatively resistant to the hypnotic actions of volatile anesthetics. METHODS: Patients with a history of AWR and matched controls from three randomized clinical trials investigating prevention of AWR were compared for relative risk of AWR. Anesthetic management was compared with the use of the Hotelling's T statistic. A linear mixed model, including previously identified covariates, assessed the effects of a history of AWR on the relationship between end-tidal anesthetic concentration and bispectral index. RESULTS: The incidence of AWR was 1.7% (4 of 241) in patients with a history of AWR and 0.3% (4 of 1,205) in control patients (relative risk = 5.0; 95% CI, 1.3-19.9). Anesthetic management did not differ between cohorts, but there was a significant effect of a history of AWR on the end-tidal anesthetic concentration versus bispectral index relationship. CONCLUSIONS: Surgical patients with a history of AWR are five times more likely to experience AWR than similar patients without a history of AWR. Further consideration should be given to modifying perioperative care and postoperative evaluation of patients with a history of AWR.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".