Mass Casualty Imaging—Policy, Planning, and Radiology Response to Mass Casualty Incidents
Bibliographic record
Abstract
A mass casualty incident (MCI) is an event that generates more patients at one time than locally available resources can manage using routine procedures. By their nature, many of these incidents have no prior notice but result in large numbers of casualties with injuries that range in severity. They can happen anywhere and at any time and regional hospitals and health-care providers have to mount a response quickly and effectively to save as many lives as possible. Radiologists must go from passenger to pilot when it comes to MCI planning. When involved at the hospital-wide planning stage, they can offer valuable expertise on how radiology can improve triage accuracy and at what cost in terms of time and resources and thereby contribute a pragmatic understanding of radiology's role and value during MCIs. By taking ownership of MCI planning in their own departments, radiologists can ensure that the radiology department can respond quickly and effectively to unforeseen emergencies. Well-designed radiology protocols will save lives in an MCI setting.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".