Efficacy of Remimazolam for Procedural Sedation in American Society of Anesthesiologists (ASA) I to IV Patients Undergoing Colonoscopy: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Remimazolam is made by combining midazolam and remifentanil as an alternative to conventional sedatives. To evaluate the efficacy of remimazolam for sedation in patients undergoing colonoscopy, we conducted a systematic review and meta-analysis of the available randomized controlled trials (RCTs) comparing remimazolam and midazolam. A search was conducted using PubMed, Cochrane Library, and clinicaltrial.gov from inception till December 26, 2021, for RCTs that investigated the efficacy of remimazolam during the above-mentioned procedure. There was no restriction of language. A quality assessment was performed using the Cochrane Risk-of-Bias tool. The data were pooled, and a meta-analysis was completed. The systemic review was conducted in accordance with the Preferred Reporting Items for Systemic Reviews and Meta-Analysis (PRISMA) guideline statement. Three randomized controlled trials involving 539 patients were included in the meta-analysis. Compared with midazolam during colonoscopy, remimazolam results in reduced need for top-up doses (RR= 3.45, 95% CI=1.07 to 11.14; P=0.04, I2=84%). The need for rescue medication was reduced with remimazolam as compared to midazolam (RR=2.42, 95%CI=1.04 to 5.61; P=0.04, I2=96%). There was no significant difference observed between the two drugs on completion of colonoscopy and the overall procedural sedation, but the sensitivity analysis favored remimazolam over midazolam for procedural sedation (RR=4.08, 95%CI=2.35 to 7.09; P<0.00001, I2=39%). This analysis demonstrates the advantages of remimazolam over other agents and sets a platform for relevant future studies.
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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 | 0.013 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.053 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".