Manikin physical realism for resuscitation education: A systematic review
Bibliographic record
Abstract
To evaluate the impact of higher physical realism of manikins on educational and clinical outcomes during life support education. This systematic review was conducted as part of the continuous evidence evaluation process of the International Liaison Committee on Resuscitation (ILCOR). A search of PubMed, Embase, and Cochrane was conducted from January 1, 2005 until April 30, 2024. Studies comparing training with higher physical realism manikins and lower realism manikins were eligible for inclusion. Studies comparing manikins to other forms of training (e.g. screen-based, virtual reality) were excluded. Risk of bias was assessed using Cochrane Risk of Bias 2 (RoB 2) for randomized trials and Risk Of Bias In Non-Randomized Studies of Interventions (ROBINS-I) for observational studies. For outcomes reported by four or more randomized studies, random effects meta -analysis using standardized mean difference was performed. Of the 1276 articles identified and screened, 21 articles comprised the final review (19 randomized trials, 2 observational studies). Meta-analysis of eight RCTs reporting simulation skill performance in a simulated clinical scenario at course conclusion demonstrated a benefit from the use of higher- realism manikins compared with lower realism manikins (standardized mean difference 0.66, 95% CI 0.08 – 1.25). Meta-analysis of seven RCTs reporting knowledge at course conclusion showed no significant difference between the use of both types of manikins. Significant risk of bias and a high degree of heterogeneity were found among the included studies. This systematic review found that higher manikin realism during resuscitation training was associated with improved simulated clinical scenario performance at course conclusion; without an effect on knowledge at course conclusion. Future studies should examine the impact of resource requirements for high realism simulation on generalizability and implementation.
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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 | 0.011 | 0.047 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.010 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".