The ABCs of DKA: Development and Validation of a Computer-Based Simulator and Scoring System
Bibliographic record
Abstract
BACKGROUND: Clinical management of diabetic ketoacidosis (DKA) continues to be suboptimal; simulation-based training may bridge this gap and is particularly applicable to teaching DKA management skills given it enables learning of basic knowledge, as well as clinical reasoning and patient management skills. OBJECTIVES: 1) To develop, test, and refine a computer-based simulator of DKA management; 2) to collect validity evidence, according to National Standard's validity framework; and 3) to judge whether the simulator scoring system is an appropriate measure of DKA management skills of undergraduate and postgraduate medical trainees. DESIGN: After developing the DKA simulator, we completed usability testing to optimize its functionality. We then conducted a preliminary validation of the scoring system for measuring trainees' DKA management skills. PARTICIPANTS: We recruited year 1 and year 3 medical students, year 2 postgraduate trainees, and endocrinologists (n = 75); each completed a simulator run, and we collected their simulator-computed scores. MAIN MEASURES: We collected validity evidence related to content, internal structure, relations with other variables, and consequences. KEY RESULTS: Our simulator consists of six cases highlighting DKA management priorities. Real-time progression of each case includes interactive order entry, laboratory and clinical data, and individualised feedback. Usability assessment identified issues with clarity of system status, user control, efficiency of use, and error prevention. Regarding validity evidence, Cronbach's α was 0.795 for the seven subscales indicating favorable internal structure evidence. Participants' scores showed a significant effect of training level (p < 0.001). Scores also correlated with the number of DKA patients they reported treating, weeks on Medicine rotation, and comfort with managing DKA. A score on the simulation exercise of 75 % had a sensitivity and specificity of 94.7 % and 51.8%, respectively, for delineating between expert staff physicians and trainees. CONCLUSIONS: We demonstrate how a simulator and scoring system can be developed, tested, and refined to determine its quality for use as an assessment modality. Our evidence suggests that it can be used for formative assessment of trainees' DKA management skills.
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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 | 0.023 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".