Sharing is caring: How EM Sim Cases (EMSimCases.com) has created a collaborative simulation education culture in Canada
Bibliographic record
Abstract
ABSTRACT Free open access medical education (FOAM) resources in emergency medicine (EM) have grown exponentially in recent years. Within this movement, there are relatively few resources dedicated to simulation in EM. EM Sim Cases is a FOAM resource that was started in 2015 with the goal of creating a central database of simulation cases and scholarly articles that could be shared worldwide and thus reduce needless duplication of effort. Since 2015, EM Sim Cases has grown to have an annual average of 8,148 views per month from a total of 161 countries. It has an editorial team of 18 members as well as a leadership team of three. There is a robust, peer-reviewed case bank ranging in topic from neonatal resuscitation to end-of-life care as well as a number of simulation-relevant educational posts.
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 | 0.015 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.038 | 0.016 |
| Scholarly communication | 0.021 | 0.007 |
| Open science | 0.005 | 0.015 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.019 | 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".