Use of Simulation-Based Education to Improve Outcomes of Central Venous Catheterization: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
PURPOSE: Central venous catheterization (CVC) is increasingly taught by simulation. The authors reviewed the literature on the effects of simulation training in CVC on learner and clinical outcomes. METHOD: The authors searched computerized databases (1950 to May 2010), reference lists, and considered studies with a control group (without simulation education intervention). Two independent assessors reviewed the retrieved citations. Independent data abstraction was performed on study design, study quality score, learner characteristics, sample size, components of interventional curriculum, outcomes assessed, and method of assessment. Learner outcomes included performance measures on simulators, knowledge, and confidence. Patient outcomes included number of needle passes, arterial puncture, pneumothorax, and catheter-related infections. RESULTS: Twenty studies were identified. Simulation-based education was associated with significant improvements in learner outcomes: performance on simulators (standardized mean difference [SMD] 0.60 [95% CI 0.45 to 0.76]), knowledge (SMD 0.60 [95% CI 0.35 to 0.84]), and confidence (SMD 0.41 [95% CI 0.30 to 0.53] for studies with single-group pretest and posttest design; SMD 0.52 (95% CI 0.23 to 0.81) for studies with nonrandomized, two-group design). Furthermore, simulation-based education was associated with improved patient outcomes, including fewer needle passes (SMD -0.58 [95% CI -0.95 to -0.20]), and pneumothorax (relative risk 0.62 [95% CI 0.40 to 0.97]), for studies with nonrandomized, two-group design. However, simulation-based training was not associated with a significant reduction in risk of either arterial puncture or catheter-related infections. CONCLUSIONS: Despite some limitations in the literature reviewed, evidence suggests that simulation-based education for CVC provides benefits in learner and select clinical outcomes.
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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 | 0.015 | 0.052 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.025 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".