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Record W4414357382 · doi:10.1161/jaha.125.043715

Disparities in Access to Vascular Stroke Imaging and Carotid Revascularization: A Population Study

2025· article· en· W4414357382 on OpenAlexaffabout
Hardeek H. Shah, Tina He, Naomi Dyck, Jillian Stang, Dana Nicol, Christiane J. McIntosh, Stephen B. Wilton, Shelagh B. Coutts, Nishita Singh, Michael D. Hill, Aravind Ganesh

Bibliographic record

VenueJournal of the American Heart Association · 2025
Typearticle
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsUniversity of ManitobaLibin Cardiovascular Institute of AlbertaAlberta Health ServicesUniversity of Calgary
Fundersnot available
KeywordsNeurovascular bundleStroke (engine)RevascularizationCarotid arteriesReceiptPopulationVascular diseasePopulation based study

Abstract

fetched live from OpenAlex

BACKGROUND: Decisions on imaging with computed tomography angiography, magnetic resonance angiography, and ultrasound in stroke or transient ischemic attack (TIA) may be influenced by factors ranging from location-based resource considerations to patient characteristics. We investigated disparities in vascular imaging utilization and 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 TIA or ischemic stroke from April 1, 2016 to March 31, 2023. We related the vascular imaging received to year, age, sex, event diagnosis, comorbidities, distance to a comprehensive stroke center, region, and stroke center type using mixed-effects logistic regressions. We similarly examined disparities in imaging for recurrent events and in receipt of carotid endarterectomy/stenting. RESULTS: Among 47 963 patients (median age, 72, interquartile range, 61-82, 47.6% female) with stroke/TIA, patients who were female, older, and experienced minor stroke/TIA (versus major stroke) had lower odds of receiving computed tomography angiography or any neurovascular imaging, as did those presenting to nonstroke centers or rural sites (eg, 35.8% rural versus 75.3% urban, adjusted odds ratio [any imaging]:0.56, 95% CI, 0.34-0.94). Odds of receiving vascular imaging increased over time, including computed tomography angiography (2016:49.1% versus 2023:79.9%, adjusted odds ratio per-year since 2015 [computed tomography angiography], 1.18 [95% CI, 1.16-1.19]). Female sex and absent neurovascular imaging carried lower odds of carotid revascularization (2.4% female versus 4.4% male, adjusted odds ratio, 0.57 [95% CI, 0.52-0.63]). CONCLUSIONS: Despite increasing utilization of neurovascular imaging, patients who are female, older, rural, or with minor stroke/TIA remain less likely to receive neurovascular imaging, with expected implications for receiving carotid revascularization. Female patients are less likely to undergo carotid revascularization even after accounting for receipt of imaging.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.819
Threshold uncertainty score0.364

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.003
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.008
GPT teacher head0.298
Teacher spread0.290 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2025
Admission routes2
Has abstractyes

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