Combination of olanzapine with bispectral index guided anesthesia to prevent postoperative delirium in elderly patients undergoing major gastrointestinal elective surgery: a randomized, double-blind, placebo-controlled study protocol
Bibliographic record
Abstract
Introduction: Postoperative delirium (POD) is an important complication of major surgery in elderly patients. It increases morbidity and mortality, hospital stay, and total healthcare costs. Since no treatment has proven effective once POD is established, prevention is key. Evidence exists that bispectral index guided anesthesia (BIS-GA) and antipsychotics may independently reduce incidence of POD, but the efficacy of combining these preventive strategies is unknown. Objective: To compare the combination of olanzapine + BIS-GA with BIS-GA alone for prevention of POD in elderly patients undergoing major elective surgery.Methods: We propose a Phase II, multi-center, randomized, double-blind, parallel, placebo-controlled trial. The study arms will be BIS-GA + two doses of olanzapine 5mg given pre and postoperatively compared with BIS-GA + placebo in patients ≥65 years hospitalized for major elective surgery. Exclusion criteria include cardiac- and neurosurgery, dementia history, concurrent antipsychotic, anticholinergic, or sedative-hypnotic use, olanzapine allergy, delirium at hospital admission, cognitive impairment and inability to be interviewed. The primary outcome is incidence of POD diagnosed by DSM-V criteria and assessed by the Confusion Assessment Method (CAM) scale. Secondary outcomes include delirium severity, rescue therapy use, length of hospital stay and incidence of adverse events.Discussion: There is an increasing need for trials that advance knowledge in prophylactic methods to prevent delirium. By combining two preventive methods, we expect to decrease the incidence of POD, which will result in decreased morbidity, mortality, and total healthcare costs.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".