Socioeconomic Status and Care After Stroke
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Socioeconomic status is inversely associated with mortality after stroke; however, the reasons behind this finding are not well-understood. We undertook a study to determine whether posthospitalization care and medication adherence vary with neighborhood income. METHODS: We conducted a cohort study of 11 050 patients with ischemic stroke or transient ischemic attack admitted to any of 11 specialized stroke centers in Ontario, Canada, between July 1, 2003 and March 31, 2008. Socioeconomic status measured as neighborhood income quintiles was imputed from the 2006 Canadian Census. We used linkages to administrative databases to evaluate processes of stroke care and medication adherence within 1 year of discharge. We used multivariable analyses to assess whether differences in stroke care and medication adherence existed across income groups after adjustment for age, sex, stroke severity, and comorbid conditions. RESULTS: Higher income was associated with higher rates of stroke unit admission, neurology consultations, referrals to secondary prevention clinics, and physician visits after hospital discharge; however, the absolute differences in rates were small. There was no difference across income quintiles in the use of postdischarge homecare services or in adherence to antihypertensive, antithrombotic, or lipid-lowering medications. CONCLUSIONS: Higher income is associated with improvements in some aspects of stroke care delivery. However, the magnitude of the care gap across income quintiles is small and is unlikely to account for the previously observed association between socioeconomic status and survival after stroke.
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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) | 0.003 | 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".