Language discordance as a marker of disparities in cerebrovascular risk and stroke outcomes: A multi-center Canadian study
Bibliographic record
Abstract
Differences in ischemic stroke outcomes occur in those with limited English proficiency. These health disparities might arise when a patient's spoken language is discordant from the primary language utilized by the health system. Language concordance is an understudied concept. We examined whether language concordance is associated with differences in vascular risk or post-stroke functional outcomes, depression, obstructive sleep apnea and cognitive impairment. This was a multi-center observational cross-sectional cohort study. Patients with ischemic stroke/transient ischemic attack (TIA) were consecutively recruited across eight regional stroke centers in Ontario, Canada (2012 – 2018). Participants were language concordant (LC) if they spoke English as their native language, ESL if they used English as a second language, or language discordant (LD) if non-English speaking and requiring translation. 8156 screened patients. 6,556 met inclusion criteria: 5067 LC, 1207 ESL and 282 LD. Compared to LC patients: (i) ESL had increased odds of diabetes (OR = 1.28, p = 0.002), dyslipidemia (OR = 1.20, p = 0.007), and hypertension (OR = 1.37, p<0.001) (ii) LD speaking patients had an increased odds of having dyslipidemia (OR = 1.35, p = 0.034), hypertension (OR = 1.37, p<0.001), and worse functional outcome (OR = 1.66, p<0.0001). ESL (OR = 1.88, p<0.0001) and LD (OR = 1.71, p<0.0001) patients were more likely to have lower cognitive scores. No associations were noted with obstructive sleep apnea (OSA) or depression. Measuring language concordance in stroke/TIA reveals differences in neurovascular risk and functional outcome among patients with limited proficiency in the primary language of their health system. Lower cognitive scores must be interpreted with caution as they may be influenced by translation and/or greater vascular risk. Language concordance is a simple, readily available marker to identify those at risk of worse functional outcome. Stroke systems and practitioners must now study why these differences exist and devise adaptive care models, treatments and education strategies to mitigate barriers influenced by language discordance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".