Survival analysis of COVID-19 versus non-COVID-19 patients requiring intensive care for acute respiratory distress syndrome: An observational retrospective study
Bibliographic record
Abstract
Background/Aim: This study analyzed clinical factors impacting the survival of COVID-19 patients with acute respiratory distress síndrome, or ARDS (CARDS) to ICU compared to non-COVID-19 ARDS patients. Methods: Clinical variables from 1,008 CARDS cases and 332 ARDS cases were computed using learning algorithms. The multivariable Cox proportional hazards regression models with the enter method evaluated risk factors and ICU mortality relationships. The survival analysis was completed with Kaplan-Meier and the log-rank tests. Results: A Random Forest model revealed that mechanical ventilation-related factors, oxygenation, blood pH, superinfection, shock, and ICU length of stay have the greatest effects on ICU survival. According to a multivariate Cox model, reintubation and a high-flow nasal cannula were essential for survival in CARDS patients during the ICU stay. The length of stay in the ICU diminishes in patients older than 45 years, regardless of the source of ARDS. Conclusion: This study gives recommendations for the respiratory care of ARDS in COVID-19 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".