Strategies to Inform Allocation of Stockpiled Ventilators to Healthcare Facilities During a Pandemic
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
During a severe pandemic, especially one causing respiratory illness, many people may require mechanical ventilation. Depending on the extent of the outbreak, there may be insufficient capacity to provide ventilator support to all of those in need. As part of a larger conceptual framework for determining need for and allocation of ventilators during a public health emergency, this article focuses on the strategies to assist state and local planners to allocate stockpiled ventilators to healthcare facilities during a pandemic, accounting for critical factors in facilities' ability to make use of additional ventilators. These strategies include actions both in the pre-pandemic and intra-pandemic stages. As a part of pandemic preparedness, public health officials should identify and query healthcare facilities in their jurisdiction that currently care for critically ill patients on mechanical ventilation to determine existing inventory of these devices and facilities' ability to absorb additional ventilators. Facilities must have sufficient staff, space, equipment, and supplies to utilize allocated ventilators adequately. At the time of an event, jurisdictions will need to verify and update information on facilities' capacity prior to making allocation decisions. Allocation of scarce life-saving resources during a pandemic should consider ethical principles to inform state and local plans for allocation of ventilators. In addition to ethical principles, decisions should be informed by assessment of need, determination of facilities' ability to use additional ventilators, and facilities' capacity to ensure access to ventilators for vulnerable populations (eg, rural, inner city, and uninsured and underinsured individuals) or high-risk populations that may be more susceptible to illness.
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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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