Surge Mechanical Ventilation for the COVID-19 Surge and Future Pandemics—Time to Reframe the Strategy
Bibliographic record
Abstract
In the spring of 2020, US regions impacted early by COVID-19, such as northern New Jersey and New York City, exceeded the usual mechanical ventilation capability at numerous hospitals. As COVID-19 spread to other communities with limited immunity, similar challenges to provide surge mechanical ventilation were reported. Hospitals, health care systems, and jurisdictional authorities sought to purchase more mechanical ventilators and quickly realized that the supply chain could not accommodate the perceived need. Numerous creative engineering ideas were proposed to augment the supply of resuscitators or mechanical ventilators. Tremendous public and governmental effort focused on preventing shortages of these potentially life-saving devices and on strategies to ration them should demand exceed supply. To achieve this end, accurate determination of both the quantity and geographic distribution of ventilators at US hospitals was crucial. The last comprehensive assessment of US ventilators was completed more than a decade ago during the H1N1 pandemic. In JAMA Network Open, Tsai et al 1 provide an updated evaluation using questions added to the American Hospital Association's Annual Survey. The reported quantities, although extrapolated estimates from incomplete data and therefore subject to bias, are an important update.
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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 | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".