Early Improvement of Acute Respiratory Distress Syndrome in Patients With COVID-19 in the Intensive Care Unit: Retrospective Analysis
Bibliographic record
Abstract
BACKGROUND: Since the start of the COVID-19 pandemic, there have been over 2 million deaths globally. Acute respiratory distress syndrome (ARDS) may be the main cause of death. OBJECTIVE: This study aimed to describe the clinical features, outcomes, and ARDS characteristics of patients with COVID-19 admitted to the intensive care unit (ICU) in Chongqing, China. METHODS: The epidemiology of COVID-19 from January 21, 2020, to March 15, 2020, in Chongqing, China, was analyzed retrospectively, and 75 ICU patients from two hospitals were included in this study. On day 1, 56 patients with ARDS were selected for subgroup analysis, and a modified Poisson regression was performed to identify predictors for the early improvement of ARDS (eiARDS). RESULTS: Chongqing reported a 5.3% case fatality rate for the 75 ICU patients. The median age of these patients was 57 (IQR 25-75) years, and no bias was present in the sex ratio. A total of 93% (n=70) of patients developed ARDS during ICU stay, and more than half had moderate ARDS. However, most patients (n=41, 55%) underwent high-flow nasal cannula oxygen therapy, but not mechanical ventilation. Nearly one-third of patients with ARDS improved (arterial blood oxygen partial pressure/oxygen concentration >300 mm Hg) in 1 week, which was defined as eiARDS. Patients with eiARDS had a higher survival rate and a shorter length of ICU stay than those without eiARDS. Age (<55 years) was the only variable independently associated with eiARDS, with a risk ratio of 2.67 (95% CI 1.17-6.08). CONCLUSIONS: A new subphenotype of ARDS-eiARDS-in patients with COVID-19 was identified. As clinical outcomes differ, the stratified management of patients based on eiARDS or age is highly recommended.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".