Disparities in Access to Vascular Stroke Imaging and Carotid Revascularization: A Population Study
Bibliographic record
Abstract
Abstract Background CT angiography (CTA), MR angiography (MRA), and ultrasound are noninvasive vascular imaging modalities used in the investigation of stroke or transient ischemic attack (TIA). Imaging decisions may be influenced by factors ranging from location-based resource considerations to patient characteristics. The aim of this study was to investigate disparities in vascular imaging utilization and subsequent carotid revascularization over 7 years in a Canadian province (Alberta, population:4.4 million). Methods We used provincial administrative data encompassing patients presenting to hospital or emergency/urgent-care facilities with a diagnosis of TIA or ischemic stroke from 1-April-2016 to 31-Mar-2023 and related the vascular imaging received (CTA/MRA/ultrasound/none) to age, sex, region (rural vs urban), diagnosis (ischemic stroke vs minor stroke/TIA), comorbidities, center type, and year using multivariable logistic regressions. We explored whether these variations persisted in recurrent events and investigated the odds of carotid endarterectomy/stenting using similar regression models. Results Among 47,963 patients (median age: 72, interquartile range: 21, 47.6% female) with TIA/stroke, those older than seventy-one, with minor stroke/TIA, and with specific comorbidities had significantly lower odds of receiving CTA or any neurovascular imaging, as were those in rural sites or hospitals not designated as Comprehensive Stroke Centers (CSCs, e.g. aOR-CTA [stroke unit-equivalent care vs CSC]: 0.20, 95%CI:0.13-0.30). Female patients were less likely to undergo CTA or any vascular imaging (66.4% female vs 71.1% male, aOR:0.84, 95%CI:0.81-0.88). Those presenting in more recent years had higher odds of receiving CTA (aOR-per-additional-year:1.15, 95%CI:1.14-1.17) or any neurovascular imaging (aOR:1.13, 95%CI:1.11-1.14). Female sex was associated with lower odds of carotid revascularization, as were patients with minor stroke/TIA, atrial fibrillation, care at non-CSC centers, and absence of vascular imaging (e.g. aOR[female vs male]:0.57, 95%CI:0.51-0.64). Conclusions We found important demographic and geographic disparities in vascular imaging utilization despite increasing utilization over time; similar disparities were also seen in carotid revascularization.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".