Retention of Critical Procedural Skills After Simulation Training: A Systematic Review
Bibliographic record
Abstract
OBJECTIVE: While short-term gains in performance of critical emergency procedures are demonstrated after simulation, long-term retention is relatively uncertain. Our objective was to determine whether simulation of critical emergency procedures promotes long-term retention of skills in nonsurgical physicians. METHODS: We searched multiple electronic databases using a peer-reviewed strategy. Eligible studies 1) were observational cohorts, quasi-experimental or randomized controlled trials; 2) assessed intubation, cricothyrotomy, pericardiocentesis, tube thoracostomy, or central line placement performance by nonsurgical physicians; 3) utilized any form of simulation; and 4) assessed skill performance immediately after and at ≥ 3 months after simulation. The primary outcome was skill performance at or above a preset performance benchmark at ≥ 3 months after simulation. Secondary outcomes included procedural skill performance at 3, 6, and ≥ 12 months after simulation. RESULTS: We identified 1,712 citations, with 10 being eligible for inclusion. Methodologic quality was moderate with undefined primary outcomes; inadequate sample sizes; and use of nonstandardized, unvalidated tools. Three studies assessed performance to a specific performance benchmark. Two demonstrated maintenance of the minimum performance benchmark while two demonstrated significant skill decay. A significant decline in the mean performance scores from immediately after simulation to 3, 6, and ≥ 12 months after simulation was observed in four of four, three of four, and two of five studies, respectively. Scores remained significantly above baseline at 3, 6, and ≥ 12 months after simulation in three of four, three of four, and four of four studies, respectively. CONCLUSION: There were a limited number of studies examining the retention of critical skills after simulation training. While there was some evidence of skill retention after simulation, overall most studies demonstrated skill decline over time.
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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 | 0.010 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.005 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".