Emergency imaging after a mass casualty incident: role of the radiology department during training for and activation of a disaster management plan
Bibliographic record
Abstract
In the setting of mass casualty incidents (MCIs), hospitals need to divert from normal routine to delivering the best possible care to the largest number of victims. This should be accomplished by activating an established hospital disaster management plan (DMP) known to all staff through prior training drills. Over the recent decades, imaging has increasingly been used to evaluate critically ill patients. It can also be used to increase the accuracy of triaging MCI victims, since overtriage (falsely higher triage category) and undertriage (falsely lower triage category) can severely impact resource availability and mortality rates, respectively. This article emphasizes the importance of including the radiology department in hospital preparations for a MCI and highlights factors expected to influence performance during hospital DMP activation including issues pertinent to effective simulation, such as establishing proper learning objectives. After-action reviews including performance evaluation and debriefing on issues are invaluable following simulation drills and DMP activation, in order to improve subsequent preparedness. Historically, most hospital DMPs have not adequately included radiology department operations, and they have not or to a little extent been integrated in the DMP activation simulation. This article aims to increase awareness of the need for radiology department engagement in order to increase radiology department preparedness for DMP activation after a MCI occurs.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".