Efficacy of dexmedetomidine in prevention of junctional ectopic tachycardia and acute kidney injury after pediatric cardiac surgery: A meta‐analysis
Bibliographic record
Abstract
OBJECTIVE: We conducted a meta-analysis to evaluate the effects of prophylactic perioperative dexmedetomidine administration on postoperative junctional ectopic tachycardia (JET) and acute kidney injury (AKI) in pediatric patients having undergone cardiac surgery. DESIGN: This systematic review was registered with PROSPERO (CRD42017083880). Databases including PubMed, Cochrane Central Register of Controlled Trials, and Web of Science were searched for randomized controlled trials (RCTs) and observational cohort studies from its inception to March 2018. Two reviewers independently screened literature, extracted data, and assessed the quality of included studies using the Jadad scale and Newcastle-Ottawa score. Meta-analysis was then conducted by RevMan 5.3 and Stata 12.0 software. P value < .05 was considered significant. RESULTS: A total of nine eligible studies (5 RCTs and 4 observational studies) comprising 1851 patients were selected for the final analysis. The results of meta-analysis showed that dexmedetomidine significantly reduced the incidence of postoperative JET (OR =0.35, 95% CI: 0.22 to 0.53, P < .00001), but there was no significant difference between groups in AKI (OR =0.44, 95% CI: 0.19 to 1.04, P = .06) and all-cause mortality (OR =0.87, 95% CI: 0.35 to 2.14, P = .77). CONCLUSIONS: The administration of perioperative dexmedetomidine effectively prevents JET in pediatric patients undergoing cardiac surgery but has no significant effect on postoperative renal function. However, the quality of evidence for these findings is low; thus, future larger scale randomized studies are needed to verify the real clinical effects of dexmedetomidine prophylaxis in pediatric patients.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.065 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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".