Establishing the validity and reliability of computer‐based simulation for cerebral angiography using the ANGIO Mentor Express
Bibliographic record
Abstract
Computer‐based simulation (CBS) is increasingly used in medical education, but evidence for its efficacy has been limited and inconsistent. Deficiencies and heterogeneity in validity and reliability study methodologies have been cited as major limitations to drawing conclusions regarding the effectiveness of CBS training. Since untested assessment methodologies can lead to reporting inaccurate performance data, it is crucial to validate CBS systems and their assessment instruments before examining the effectiveness of CBS training. This study establishes the validity (face, content and construct) and reliability (test‐retest) of a CBS system (ANGIO Mentor Express) for left and right cerebral angiography (CA). Face and content validities were established by asking experienced catheter‐based physicians to judge the overall realism of the simulated CA procedures and evaluate the appropriateness of the assessment instruments. Construct validity was assessed by comparing the performance of an expert cohort (experienced physicians) to a novice cohort (medical students and residents) of subjects. Test‐retest reliability was established by correlating performances on the simulated left CA with the right CA. Confirmation of the validity and reliability of the CBS system and its assessment instruments assures the integrity of future studies evaluating the efficacy of CBS training for CA. Grant Funding Source : None
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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 | 0.044 | 0.120 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".