A90 A PROPENSITY SCORE-MATCHED, STATE-LEVEL COMPARISON OF INPATIENT CIRRHOSIS OUTCOMES IN ENGLISH VS NON-ENGLISH SPEAKING PATIENTS
Bibliographic record
Abstract
Abstract Background United States (US) citizens speaking a foreign language at home has increased by 192% from 1980 to 2018. Aims With the increase in multiculturalism and ongoing concerns for health disparities in the US, we sought to compare inpatient outcomes between non-English speaking and English-speaking patients with cirrhosis. Methods The 2013–14 Maryland State inpatient databases were used to compare inpatient outcomes in adult patients with cirrhosis. The analysis cohort was identified using a validated algorithm of ICD-9 codes. Cirrhosis patients were stratified based on primary language into non-English-speaking patients [NESP] vs English-speaking patients [ESP]. A 1:3 propensity score matching analysis based on possible confounders was used to finalize the analysis cohort. The primary outcome (all-cause in-hospital mortality) and secondary outcomes including 30-day all-cause readmission rates, length of stay, total hospitalization charges were then compared between groups. Results In the study period, 3,035 NESP vs 21,212 ESP discharges were identified. We matched 1,659 NESP with 4,928 ESP using a 1:3 algorithm. Table 1 highlights demographic data. In the unmatched analysis, all-cause mortality was higher in the ESP cohort compared to NESP (6.71% vs 5.73%, p=0.046). However, after propensity-matching, inpatient mortality rate became comparable between both groups (6.45% vs 6.51%, p=0.9). Thirty day all-cause readmission rates were also similar between ESP vs NESP (4.87% vs 4.28%, p=0.18). Median length of stay in the ESP group was 4 days (IQR 3–8) vs 5 days (IQR 3–7) in the NESP group, while median total charges in ESP were $55,984 (IQR $33,897-$98,679) compared to $61,262 (IQR $36,228-$$108,369) in NESP. Conclusions While significant differences in socioeconomic status and payer type exist between non-English and English speaking cirrhosis patients, these do not appear to negatively impact inpatient outcomes including all-cause inpatient mortality, 30-day readmission rates, length of stay, total hospital charges. 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 | 0.002 | 0.005 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".