Rethinking Surge Preparedness After COVID-19: Effective Patient Load Balancing Within Health Systems and Beyond
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
Within weeks of New York State's first confirmed case of COVID-19, New York City became the epicenter of the nation's COVID-19 pandemic. With more than 80,000 COVID-19 hospitalizations during the first wave alone, hospitals in downstate New York were forced to adapt existing procedures to manage the surge and care for patients facing a novel disease. Given the unprecedented surge, effective patient load balancing-moving patients from a hospital with diminishing capacity to another hospital within the same health system with relatively greater capacity-became chief among the capabilities required of New York health systems. The Greater New York Hospital Association invited members of downstate New York's 6 largest health systems to talk about how each of their systems evolved their patient load balancing procedures throughout the pandemic. Informed by their insights, experiences, lessons learned, and collaboration, we collectively present a set of consensus recommendations and best practices for patient load balancing at the facility and health system level, which may inform regional approaches to patient load balancing.
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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 | 0.010 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.007 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 it