Intubation Time, Lung Mechanics and Outcome in COVID-19 Patients Suffering Acute Respiratory Distress Syndrome: A Single-Center Study
Bibliographic record
Abstract
Background: We examined the effect of intubation time and the lung mechanics on clinical outcomes in coronavirus disease 2019 (COVID-19) patients. Methods: Based on the patient's hospital admission, intubation time was defined as early (≤ 2 days) or late (> 2 days). Patients were further divided into three groups; early (≤ 3 days), late (4 - 6 days), and very late (> 6 days) intubated. Results: A total of 194 patients were included; 66.5% male, median age 65 years. Fifty-eight patients (29.9%) were intubated early and 136 (70.1%) late. Early intubated patients revealed lower mortality (44.8% vs. 72%, P < 0.001), were younger (60 vs. 67, P = 0.002), had lower sequential organ failure assessment (SOFA) scores (6 vs. 8, P = 0.002) and higher lung compliance on admission days 1, 6 and 12 (42 vs. 36, P = 0.006; 40 vs. 33, P < 0.001; and 37.5 vs. 32, P < 0.001, respectively). Older age (adjusted odds ratio (aOR) = 1.15, P < 0.001), intubation time (aOR = 1.15, P = 0.004), high SOFA scores (aOR = 1.81, P < 0.001), low partial pressure of oxygen (PaO 2 )/fractional inspired oxygen tension (FiO 2 ) ratio (aOR = 0.96, P = 0.001), and low lung compliance on admission days 1 and 12 (aOR = 1.12, P = 0.012 and aOR = 1.14, P < 0.001, respectively) were associated with higher mortality. Very late and late intubated patients had higher mortality rates than patients intubated early (78.4% vs. 63.4% vs. 44.6%, respectively, P < 0.001). Conclusions: Among COVID-19 intubated patients, age, late intubation, high SOFA scores, low PaO 2 /FiO 2 ratio, and low lung compliance are associated with higher intensive care unit (ICU) mortality. J Clin Med Res. 2024;16(1):15-23 doi: https://doi.org/10.14740/jocmr4984
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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 | 0.001 | 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) | 0.002 | 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".