Efficacy of Ondansetron in the Prevention or Treatment of Post-operative Delirium— a Systematic Review
Bibliographic record
Abstract
BACKGROUND: Post-operative delirium (POD) is associated with higher rates of functional decline and death. Ondansetron is a serotonin antagonist which could represent a therapeutic or preventive option in POD. METHODS: A systematic review of MEDLINE, EMBASE, CENTRAL, and PsychINFO was performed. Three randomized controlled trials (RCTs) met inclusion criteria (intervention of ondansetron compared to a control group). RESULTS: Two RCTs examined ondansetron for the treatment of POD in patients after cardiac or post-trauma surgery in the ICU. Studies assessed either a one-time dose or doses for 3 days of ondansetron or haloperidol IV. They suggested similar reductions in average delirium scores and rates in both interventions, although one study suggested ondansetron to be associated with higher rates of rescue haloperidol use. One RCT examined prophylactic ondansetron versus placebo IV, for five days postoperatively, to prevent POD in orthopedic patients. There were significantly fewer delirious patients in the ondansetron group. In general, studies had major methodological limitations and were very heterogenous in study tools, interventions used, and populations studied. CONCLUSIONS: Ondansetron may be an effective agent for the prevention or treatment of POD, but studies are few and of poor quality, thus making the conclusions tenuous. Further large RCTs are needed.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".