The Impact of Patients' Primary Language on Inpatient Gastrointestinal Bleeding Outcomes
Bibliographic record
Abstract
BACKGROUND: The impact of English proficiency on gastrointestinal bleeding (GIB) outcomes remains unclear. In this analysis, we compare inpatient GIB outcomes between patients with English as their primary language (EPL) and those with a primary language other than English (PLOE). METHODS: Using the 2019 State Inpatient Databases for New Jersey, Maryland, and Michigan, we created an analysis cohort of GIB hospitalizations using International Classification of Diseases, 10th Revision codes. Patients were stratified by primary language (EPL vs PLOE) and type of bleeding (variceal upper GI bleeding [VUGIB], nonvariceal upper GI bleeding [NVUGIB], and lower GI bleeding (LGIB)]. Regression analyses were used to compare mortality, 30-day readmissions, and length of stay. Adjusted odds ratios (aORs) and 95% confidence intervals (CIs) were reported. P < 0.05 was considered statistically significant. RESULTS: In the cohort, 5.5%-10% of the patients spoke a primary language other than English. Endoscopy utilization was lower among patients with PLOE vs EPL for NVUGIB (17.2% vs 21.2%, P < 0.001) and LGIB (26.3% vs 29.2%, P = 0.027). Patients with PLOE had higher odds of dying of VUGIB (aOR 1.45, 95% CI 1.16-2.48) and LGIB (aOR 1.71, 95% CI 1.22-2.12). Patients with PLOE were also more likely to be readmitted after NVUGIB (aOR 1.75, 95% CI 1.64-1.81). However, after controlling for the percentage of patients with PLOE discharged from each hospital, the disparities in mortality and readmissions were no longer detected. DISCUSSION: Disparities exist in GIB outcomes among patients with PLOE, but these gaps narrow at hospitals with higher percentages of patients with PLOE. Cultural and linguistic competence may improve outcomes in this vulnerable group.
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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 | 0.008 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".