A Post-operative Masquerade: Simulation-based Scenario Challenging Clinical Clerks to Recognize an Atypical Presentation of Myocardial Infarction
Bibliographic record
Abstract
Post-operative myocardial infarctions (MI) are a challenging diagnosis due to the alterations in the presenting complaint compared to an acute MI. Patients may be asymptomatic due to their anesthetics and sedatives from their operation which may create clinical confusion. As such, there is an increased risk for delayed administration of reperfusion therapies in this patient population which has shown to increase morbidity and mortality. It is anticipated that the difficulty of recognizing a post-operative MI would be exacerbated for clinical clerks due to their lack of clinical experience and overstimulation. Fortunately, the use of simulation-based learning has been proven to be a useful teaching tool to help clinical clerks manage medical problems in a controlled environment. This technical report describes a simulation case designed to enhance the recognition and response to a post-operative MI by a third-year clinical clerk. In this scenario, a 56-year-old male accountant presents with shortness of breath while recovering in the orthopaedic ward 12 hours following a total knee replacement (TKR). The clinical clerks are expected to conduct an independent follow-up prior to finishing their shift during which the patient begins complaining of shortness of breath. The clerk is required to order an electrocardiogram (ECG) for further analysis which reveals an anterior ST-segment elevation. Once recognized, a request for the crash cart and patient handover to the senior physician are expected.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".