Gastrointestinal Bleeding in Patients With Acute Respiratory Distress Syndrome: A National Database Analysis
Bibliographic record
Abstract
BACKGROUND: The goal of our study was to determine the impact of gastrointestinal bleeding (GIB) on in-hospital outcomes among acute respiratory distress syndrome (ARDS) patients, and subsequently determine the potential risk factors for the development of GIB. METHODS: ARDS patients with and without GIB were identified using the National Inpatient Sample (2002 - 2012). Linear regression analysis was used to assess impact of GIB on in-hospital mortality, length of stay and total charges. Univariate logistic regression was used to determine associated odds ratios (OR) for causes of ARDS and common comorbid conditions. RESULTS: We identified 149,190 ARDS patients. The incidence of GIB was the highest among patients > 60 years (P < 0.001). GIB was associated with longer hospitalization days (7.3 days versus 11.9 days, P < 0.001), higher mortality (11% versus 27%, P < 0.001) and greater economic burden ($82,812 versus $45,951, P < 0.001). GIB was common in cirrhosis (OR: 8.3), peptic ulcer disease (OR: 3.7), coagulopathy disorders (OR: 3.003), thrombocytopenia (OR: 2.6), anemia (OR: 2.5) and atrial fibrillation (OR: 1.5). ARDS secondary to aspiration pneumonia (OR: 2.0), pancreatitis (OR: 2.0), sepsis (OR: 1.6) and community acquired pneumonia (OR: 0.8) was more likely to have GIB. CONCLUSION: Our study demonstrates that GIB in ARDS patients is associated with significant increased mortality, hospitalization and health care cost.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".