Emergency Department Presentation of a Patient with Altered Mental Status: A Simulation Case for Training Residents and Clinical Clerks
Bibliographic record
Abstract
Emergency physicians frequently are required to perform timely assessments on patients who are unable to provide a comprehensive history due to an altered level of responsiveness. The etiology of their altered mental status (AMS) causes a diagnostic dilemma due to its wide differential diagnosis. Physicians must use a timely combination of collateral history, physical examination skills, and investigations to diagnose the cause of the patient's AMS, as many of the potential etiologies can be life-threatening if not quickly managed. For this reason, training learners to perform the required actions accurately and effectively proves difficult during real-life emergencies, where an individual's life may be at risk. Simulation-based education (SBE) offers one solution to this challenge. It allows learners to build confidence by dealing with life-threatening conditions in a safe environment and has been shown to be superior to other forms of clinical training. This scenario explores learners' comfort in some less-practiced, but very important, areas of medicine including obtaining consent for treatment from a substitute decision maker (SDM), explaining various goals of care, and eliciting an advanced care directive from the SDM. Learners and physicians in all fields of medicine must be able to confidently discuss these subjects with patients and their families in order to provide individualized and appropriate management. In this simulation, learners will have the opportunity to explore an unusual AMS presentation and develop their clinical and communication skills by working as a team to manage the patient.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| 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".