Role of Intermediate Care Unit Admission and Noninvasive Respiratory Support during the COVID-19 Pandemic: A Retrospective Cohort Study
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic has led to shortage of intensive care unit (ICU) capacity. We developed a triage strategy including noninvasive respiratory support and admission to the intermediate care unit (IMCU). ICU admission was restricted to patients requiring invasive ventilation. OBJECTIVES: The aim of this study is to describe the characteristics and outcomes of patients admitted to the IMCU. METHOD: Retrospective cohort including consecutive patients admitted between March 28 and April 27, 2020. The primary outcome was the proportion of patients with severe hypoxemic respiratory failure avoiding ICU admission. Secondary outcomes included the rate of emergency intubation, 28-day mortality, and predictors of ICU admission. RESULTS: One hundred fifty-seven patients with COVID-19-associated pneumonia were admitted to the IMCU. Among the 85 patients admitted for worsening respiratory failure, 52/85 (61%) avoided ICU admission. In multivariate analysis, PaO2/FiO2 (OR 0.98; 95% CI: 0.96-0.99) and BMI (OR 0.88; 95% CI: 0.78-0.98) were significantly associated with ICU admission. No death or emergency intubation occurred in the IMCU. CONCLUSIONS: IMCU admission including standardized triage criteria, self-proning, and noninvasive respiratory support prevents ICU admission for a large proportion of patients with COVID-19 hypoxemic respiratory failure. In the context of the COVID-19 pandemic, IMCUs may play an important role in preserving ICU capacity by avoiding ICU admission for patients with worsening respiratory failure and allowing early discharge of ICU patients.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".