Preparing your emergency department for disaster: Optimizing surge capacity during mass casualty events
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Mass casualty events can cause patient surges within healthcare facilities. These surges can be limited to hours or continue for days or weeks. As emergency departments are the front doors to the healthcare system, it is critical that they are prepared to accept patient surges. Focusing plans on optimizing space, staff, and supplies is critical to a successful response. Boarded or non-emergent patients must be diverted, discharged, and decanted from the emergency department to expand resuscitation space. If inadequate, non-clinical space may be required for patient care. Staff call-in lists should be maintained, and in-house berthing for staff during prolonged responses may be necessary. Further, identifying the spectrum of care, from conventional to crisis, is necessary to thrive during a disaster response: staff must understand that business as usual will not be compatible with austere disaster response before levels of care begin to decline.
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.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it