Survival in critically ill admissions with and without COVID-19 at an academic medical center during the height of the pandemic
Bibliographic record
Abstract
Objective: Early reports demonstrate that patients with Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) infection have high rates of hospitalization, intensive care unit (ICU) admission, and death. We sought to examine characteristics of ICU admissions with and without Coronavirus 2019 (COVID-19) and to compare outcomes between these two critically ill cohorts.Methods: A retrospective analysis of 600 unique adult ICU admissions was conducted at an academic medical center in Boston, MA from March 22 to May 31, 2020.Results: Of 600 ICU admissions, 170 (28.3%) tested positive for COVID-19. Those with COVID-19 had greater severity of illness and were more likely to require mechanical ventilation (MV). Hospital and ICU mortality rates were greater in the COVID-19 group (22.4% vs. 9.5%; 18.2% vs. 7.2%, respectively), but lower than previous reports. Unadjusted odds ratio (OR) for COVID-19 as a predictor of hospital mortality was 2.73 (95% CI 1.68 to 4.43), but when accounting for clinical characteristics and severity of illness, adjusted OR for hospital mortality was no different (1.09 [95% CI 0.50 to 2.41]) among those with and without COVID-19.Conclusions: COVID-19 admissions had greater severity of illness and suffered higher crude mortality rates compared to the non-COVID-19 cohort. However, there was no significant difference in the adjusted OR for hospital mortality between patients with and without COVID-19. This novel finding may be attributed to the “learning curve” from other healthcare system experiences, early hospital-wide preparation, and dedicated intensive care.
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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 | 0.005 |
| 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 | 0.001 |
| 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".