Initial Clinical Impressions of the Critical Care of COVID-19 Patients in Seattle, New York City, and Chicago
Bibliographic record
Abstract
Since the first recognition of a cluster of novel respiratory viral infections in China in late December 2019, intensivists in the United States have watched with growing concern as infections with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus-now named coronavirus disease of 2019 (COVID-19)-have spread to hospitals in the United States. Because COVID-19 is extremely transmissible and can progress to a severe form of respiratory failure, the potential to overwhelm available critical care resources is high and critical care management of COVID-19 patients has been thrust into the spotlight. COVID-19 arrived in the United States in January and, as anticipated, has dramatically increased the usage of critical care resources. Three of the hardest-hit cities have been Seattle, New York City, and Chicago with a combined total of over 14,000 cases as of March 23, 2020.In this special article, we describe initial clinical impressions of critical care of COVID-19 in these areas, with attention to clinical presentation, laboratory values, organ system effects, treatment strategies, and resource management. We highlight clinical observations that align with or differ from already published reports. These impressions represent only the early empiric experience of the authors and are not intended to serve as recommendations or guidelines for practice, but rather as a starting point for intensivists preparing to address COVID-19 when it arrives in their community.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".