Medical Simulation: The Least Advertised and Most Versatile Weapon in Pandemic
Bibliographic record
Abstract
"From America to Europe, world leaders declared war on coronavirus disease (COVID-19), an invisible, poorly understood enemy. Most countries and health-care providers were baffled by the rapid pace of the pandemic. The World Health Organization (WHO) declared the outbreak as a public health emergency of international concern on January 30th. Through simulation scenarios published the next day, it highlighted the need for being prepared and organized to march off into battle. Initial research focused on understanding the virus, testing drugs, and developing strategies. In this fight against the pandemic, a “new” medical weapon has emerged: medical simulation. Simulation is an agile, concrete, and mobile multi-tool (1), useful for learning in all domains (knowledge, skills, and behavior) (2). It may be used to safely train professionals in real-like conditions (3) at several levels. In times of crisis, simulation is an ideal medium to update and enhance competencies and adapt practices, particularly constantly evolving practices. At a team level, simulation helps to face complex clinical situations like cardiac arrest in an infected patient (4) or prone positioning for managing respiratory distress. The ability of simulation to promote teamwork is also decisive (5). At a department level, a simulation may optimize the patients and working flow within new constraints. At a personal level, simulation equips one with behaviors and skills for safely donning and doffing, as well as technical skills such as intubation with minimal aerosol exposure. Therefore, simulation in a COVID-19 context is akin to a “Swiss Army knife,” as it carries with it extreme utility and applies to several scenarios at hand. This weapon is loaded in simulation centers and its use adapted on sites (in situ). On the basis of the experience of two universities located in heavily affected areas (University of Lorraine, Nancy, France, and McGill University, Montréal, Canada), we describe here how this “Swiss Army knife” helped in adapting the answer to COVID-19 with two preferred complementary approaches: ex situ and in situ simulations, respectively."@eng
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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 | 0.007 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".