N-acetyl-cysteine in Intensive Care Unit Patients with Acute Respiratory Distress Syndrome due to COVID-19: A Retrospective Cohort Study
Bibliographic record
Abstract
COVID-19-related acute respiratory distress syndrome (ARDS) is linked to mortality, primarily due to a cytokine storm, oxidative stress imbalance, and pro-thrombotic state.PurposeWe assessed the potential association between N-acetyl-cysteine (NAC) and clinical outcomes in critically ill subjects with COVID-19-related ARDS.Material and MethodsWe included subjects with confirmed COVID-19 who were admitted to our ICU between March 1, 2020, and January 31, 2021, due to ARDS and necessitating invasive mechanical ventilation (IMV). Subjects who received standard of care (SOC) were compared with subjects who additionally received NAC 600 mg bid orally.ResultsA total of 243 subjects were included in this study. The results indicate significantly improved survival rates in the NAC plus SOC group, both in the unadjusted analysis and after adjusting for confounding factors such as ARDS severity (HR 0.48, 95% CI 0.32-0.70).ConclusionsWe found that oral administration of NAC was associated with reduced mortality in critically ill patients with COVID-19 related ARDS.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".