Association Between Hospital Type and Resilience During COVID-19 Caseload Stress
Bibliographic record
Abstract
BACKGROUND: Imbalances between hospital caseload and care resources that strained U.S. hospitals during the pandemic have persisted after the pandemic amid ongoing staff shortages. Understanding which hospital types were more resilient to pandemic overcrowding-related excess deaths may prioritize patient safety during future crises. OBJECTIVE: To determine whether hospital type classified by capabilities and resources (that is, extracorporeal membrane oxygenation [ECMO] capability, multiplicity of intensive care unit [ICU] types, and large or small hospital) influenced COVID-19 volume-outcome relationships during Delta wave surges. DESIGN: Retrospective cohort study. SETTING: 620 U.S. hospitals in the PINC AI Healthcare Database. PARTICIPANTS: Adult inpatients with COVID-19 admitted July to November 2021. MEASUREMENTS: Hospital-months were ranked by previously validated surge index (severity-weighted COVID-19 inpatient caseload relative to hospital bed capacity) percentiles. Hierarchical models were used to evaluate the effect of log-transformed surge index on the marginally adjusted probability of in-hospital mortality or discharge to hospice. Effect modification was assessed for by 4 mutually exclusive hospital types. RESULTS: = 0.32) in log surge index-mortality relationship across 4 hospital types. Results were consistent after excluding transferred patients, restricting to patients with acute respiratory failure and mechanical ventilation, and using alternative strain metrics. LIMITATION: Residual confounding. CONCLUSION: Comparably detrimental relationships between COVID-19 caseload and survival were seen across all hospital types, including highly advanced centers, and well beyond the pandemic's learning curve. These lessons from the pandemic heighten the need to minimize caseload surges and their effects across all hospital types during public health and staffing crises. PRIMARY FUNDING SOURCE: Intramural Research Program of the National Institutes of Health Clinical Center.
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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 | 0.001 | 0.002 |
| 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 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".