A224 LIVER CIRRHOSIS AND VENOUS THROMBOEMBOLISM: A NATIONAL INPATIENT SAMPLE STUDY
Bibliographic record
Abstract
Abstract Background The sequelae of decompensated cirrhosis include a reduction in both hepatic coagulation factors and platelets. While historically it was felt that patients with cirrhosis were naturally anticoagulated, recent studies have refuted this. As a result, cirrhotic patients may be a risk for development of venous thromboembolism (VTE). Conflicting data regarding VTE risks limits guidance for clinicians. Aims National Inpatient Sample (NIS) data were analyzed to compare the prevalence of VTE among hospitalized patients with and without cirrhosis. Methods NIS is a database of US inpatient admissions. The 2014 NIS database was interrogated using ICD-9-CM codes to identify adult patients with cirrhosis and VTE. Baseline characteristics for patients with and without cirrhosis were compared. Multivariate regression models identified risks of VTE adjusting for survey procedures. Data were presented with odds ratio (OR) with 95% confidence intervals (95% CI). A p-value <0.05 was statistically significant. Results 605,825 patients with cirrhosis were included. VTE occurred in 8,940 patients with cirrhosis and 627,490 controls (1.5% and 2.2% respectively). The corresponding values for PE were 0.5% and 1.1%; and for DVT were 1.1% and 1.4%. The OR for VTE in patients with cirrhosis was 0.547 [95% CI (0.520–0.576), p <0.001] when adjusting for risk factors for VTE (table 1). Conclusions Prevalence of VTE was lower among inpatients with cirrhosis compared to controls. Use of anticoagulation was not controlled as these data were not available, which could limit some associations. Further prospective studies are needed to overcome the limitations of retrospective analysis. Funding Agencies None
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".