Treatment outcomes and healthcare resource utilization in critically ill COVID-19 patients in Korea: A nationwide multicenter cohort study
Bibliographic record
Abstract
COVID-19 pandemic was accompanied by many healthcare-related issues. Concrete national data regarding the care performance of critical ill cases of COVID-19 does not exist in Korea. The current study aimed to describe the treatment outcome and healthcare resource utilization of critically ill COVID-19 patients. Our multicenter retrospective cohort study enrolled critically ill COVID-19 patients from 22 tertiary care hospitals in Korea. Inclusion criteria: (1) patients aged 19 years or older, (2) patients with laboratory-confirmed SARS-CoV-2 infection who received at least one of following initial treatments such as high-flow oxygen therapy (HFOT) or noninvasive ventilation (NIV) or invasive mechanical ventilation (IMV) or extracorporeal membrane oxygenation. During the study wave, a total of 1358 eligible participants were enrolled, with 21 institutions participating in the study. Among them, data from 1113 patients were available and analyzed. Of 921 (82.7%), 621 (55.8%) were supported by IMV. Of the 921 patients supported by HFOT or NIV, 438 (47.6%) recovered without IMV, 429 (46.6%) required IMV, and 54 died who DNR after NIV was applied. Prone position ventilation was administered to 163 (33.1%) patients with IMV and 25 (6.2%) patients with HFOT. Extracorporeal membrane oxygenation was administered to 128 (20.6%) patients treated with IMV. The overall mortality rate was 26.4%. In South Korea, mortality rates for patients with severe COVID-19 pneumonia have been shown substantial fatality, with the highest mortality rates observed in wave 3. The increased mortality rate in wave 3 could be associated with the rapid escalation of critically ill COVID-19 patients and the consequent saturation of intensive care unit capacities. Patients received NIV therapy and prone position ventilation more frequently in wave 3 as the number of cases increased.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".