Comparative Evaluation of Risk of Death in Mechanically Ventilated Patients With COVID-19 and Influenza: A Population-Based Cohort Study
Bibliographic record
Abstract
Background: Reports on the comparative mortality among mechanically ventilated patients with coronavirus disease 2019 (COVID-19) and influenza show conflicting findings, but studies focused largely on the early phase of the pandemic, using historical influenza comparators. We sought to examine the population-level comparative mortality among mechanically ventilated patients with COVID-19 during the latter pandemic years using contemporaneous influenza comparators. Methods: We used a statewide dataset to identify mechanically ventilated hospitalizations aged ≥ 18 years with COVID-19 or influenza in Texas between October 2021 and March 2023. Their comparative short-term mortality (in-hospital death or discharge to hospice) was estimated using overlap propensity score weighting (primary model), entropy balance, and hierarchical logistic models. Results: Among 22,195 mechanically ventilated hospitalizations, 19,659 (88.6%) had COVID-19 and 2,536 (11.4%) had influenza. Compared to mechanically ventilated hospitalizations with influenza, those with COVID-19 were more commonly racial or ethnic minority (49.3% vs. 48.4%) and had lower mean (standard deviation (SD)) Deyo comorbidity index (2.04 (2.03) vs. 2.53 (1.91)), but higher number of organ dysfunctions (2.60 (1.37) vs. 2.13 (1.27)), respectively. Short-term mortality among mechanically ventilated hospitalizations with COVID-19 and influenza was 49.1% vs. 20.7%. The risk of short-term mortality was attenuated but remained higher among hospitalizations with COVID-19 in the primary model (adjusted risk ratio: 1.24 (95% confidence interval (CI): 1.18 - 1.30); adjusted risk difference 8.8% (95% CI: 6.7 - 10.4)), with consistent findings in alternative models, subgroups, and sensitivity analyses. Conclusions: Population-level short-term mortality among mechanically ventilated hospitalizations with COVID-19 has been higher than that among those with influenza during the latter years of the pandemic.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".