Impact of Language Barriers on Stroke Care and Outcomes
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Language barriers may lead to poor quality of care, particularly for conditions like acute stroke for which diagnosis and treatment decision making rely on taking an accurate patient history. The purpose of this study was to determine the impact of patient language barriers on quality of stroke care and clinical outcomes. METHODS: This retrospective cohort study used data from the Registry of the Canadian Stroke Network. All Ontario patients who were admitted with acute stroke or transient ischemic attack between July 2003 and March 2008 were selected. Mortality, stroke outcomes, in-hospital complications, quality of care, and disposition were compared between those without (n=12 787) and with (n=1506) language barriers, which was defined based on the patient's preferred language. Hierarchical multivariable regression models determined the effect of language barriers, independent of baseline covariates. RESULTS: Patients with language barriers had better 7-day mortality than those without (7.0% versus 9.2%; OR, 0.69; 95% CI, 0.57-0.82; P<0.001). However, they were more likely to be discharged with a moderate-to-severe neurological deficit (65.9% versus 51.5%; OR, 1.25; 95% CI, 1.15-1.35). In-hospital complication rates did not differ, and quality of care indicators generally favored patients with language barriers. CONCLUSIONS: Patients who had language barriers had reduced mortality and better performance on some quality of care measures. These differences existed despite adjustment for many potential confounders, including ethnicity, prognostic factors, and stroke characteristics.
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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 | 0.011 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".