Non-English Primary Language
Bibliographic record
Abstract
OBJECTIVE: To examine access to cholecystectomy and postoperative outcomes among non-English primary-speaking patients. BACKGROUND: The population of U.S. residents with limited English proficiency is growing. Language affects health literacy and is a well-recognized barrier to health care in the United States of America. Historically marginalized communities are at greater risk of requiring emergent gallbladder operations. However, little is known about how primary language affects surgical access and outcomes of common surgical procedures, such as cholecystectomy. METHODS: We conducted a retrospective cohort study of adult patients after receipt of cholecystectomy in Michigan, Maryland, and New Jersey utilizing the Healthcare Cost and Utilization Project State Inpatient Database and State Ambulatory Surgery and Services Database (2016-2018). Patients were classified by primary spoken language: English or non-English. The primary outcome was admission type. Secondary outcomes included operative setting, operative approach, in-hospital mortality, postoperative complications, and length of stay. Multivariable logistics and Poisson regression were used to examine outcomes. RESULTS: Among 122,013 patients who underwent cholecystectomy, 91.6% were primarily English speaking and 8.4% were non-English primary language speaking. Primary non-English speaking patients had a higher likelihood of emergent/urgent admissions (odds ratio: 1.22, 95% CI: 1.04-1.44, P = 0.015) and a lower likelihood of having an outpatient operation (odds ratio: 0.80, 95% CI: 0.70-0.91, P = 0.0008). There was no difference in the use of a minimally invasive approach or postoperative outcomes based on the primary language spoken. CONCLUSIONS: Non-English primary language speakers were more likely to access cholecystectomy through the emergency department and less likely to receive outpatient cholecystectomy. Barriers to elective surgical presentation for this growing patient population need to be further studied.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.016 | 0.003 |
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".