The Utility of the Mini-Clinical Evaluation Exercise (Mini-CEX) in the Emergency Department: A Systematic Review and Meta-Analysis Evaluating the Readability, Feasibility, and Acceptability of Mini-CEX Utilization
Bibliographic record
Abstract
Assessment tools, such as the mini-clinical evaluation exercise (mini-CEX), have been developed to evaluate the competence of medical trainees during routine duties. However, their effectiveness in busy environments, such as the emergency department (ED), is poorly understood. This study assesses the feasibility, reliability, and acceptability of implementing the mini-CEX in the ED. PubMed, Google Scholar, ScienceDirect, Scopus, and Web of Science databases were scoured for observational and randomized trials related to our topic. Moreover, a manual search was also conducted to identify additional studies. After the literature search, data were extracted from studies that were eligible for inclusion by two independent reviewers. When applicable, meta-analyses were performed using the Comprehensive Meta-Analysis software. In addition, the methodological quality of studies was evaluated using the Newcastle-Ottawa Scale. Of the 2,105 articles gathered through database and manual searches, only four met the criteria for inclusion in the review. A combined analysis of three studies revealed that trainee-patient interactions averaged 16.05 minutes (95% CI = 14.21-17.88), and feedback was given in about 10.78 minutes (95% CI = 10.19-11.38). The completion rates for mini-CEX were high: 95.7% (95% CI = 87.6-98.6) for medical trainees and 95.8% (95% CI = 89.7-98.3) for assessors. Satisfaction with mini-CEX was notable, with 63.5% (95% CI = 51.5-74.1) of medical trainees and 75.7% (95% CI = 63.9-84.6) of assessors expressing contentment. Qualitative data from one study demonstrated that 70.6% of faculty members could allocate suitable time for mini-CEX during their clinical shifts. The mini-CEX is a feasible and acceptable assessment tool within the ED. Furthermore, there is evidence to suggest that it might be reliable.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.072 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".