Complex Thrombo-Inflammatory Responses versus Outcomes of Non-COVID-19 Community-Acquired Pneumonia and COVID-19
Why this work is in the frame
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Bibliographic record
Abstract
INTRODUCTION: The thrombo-inflammatory response and outcomes of community-acquired pneumonia (CAP) due to various organisms (non-COVID-19 CAP) versus CAP due to a single virus, SARS-CoV-2 (i.e., COVID-19) may differ. METHODS: Adults hospitalized with non-COVID-19 CAP (December 1, 2021-June 15, 2023) or COVID-19 (March 2, 2020-June 15, 2023) in Canada. We compared non-COVID-19 CAP and COVID-19 baseline, thrombo-inflammatory response, and mortality. We measured plasma cytokine and coagulation factor levels in a sample of patients, did hierarchical clustering, and compared cytokine and coagulation factor levels. RESULTS: In 2,485 patients (non-COVID-19 CAP, n = 719; COVID-19 patients, n = 2,157), non-COVID-19 CAP patients had significantly lower 28-day mortality (CAP vs. COVID-19 waves 1 and 2; 10% vs. 18% and 16%, respectively), intensive care unit admission (CAP vs. all waves; 15% vs. 39%, 37%, 33%, and 24%, respectively), invasive ventilation (CAP vs. waves 1, 2, and 3 patients; 11% vs. 25%, 20%, and 16%), vasopressor use (CAP 12% vs. 23%, 21%, and 18%), and renal replacement therapy use (CAP 3% vs. Omicron 7%). Complexity of hierarchical clustering aligned directly with mortality: COVID-19 wave 1 and 2 patients had six clusters at admission and higher mortality than non-COVID-19 CAP and Omicron that had three clusters at admission. Pooling all COVID-19 waves increased complexity with seven clusters on admission. CONCLUSION: Complex thrombo-inflammatory responses aligned with mortality of CAP. At a fundamental level, the human thrombo-inflammatory response to a brand new virus was "confused" whereas humans had eons of time to develop a more concise efficient thrombo-inflammatory host response to CAP.
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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 | 0.008 | 0.110 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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