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Record W4390586515 · doi:10.1161/strokeaha.123.045320

Sex-Based Analysis of Workflow and Outcomes in Acute Ischemic Stroke Patients Treated With Alteplase Versus Tenecteplase

2024· article· en· W4390586515 on OpenAlexaffabout
Diana Kim, Nishita Singh, Luciana Catanese, Amy Yu, Andrew M. Demchuk, Mar Irida Lloret-Villas, Yan Deschaintre, Shelagh B. Coutts, Houman Khosravani, Ramana Appireddy, F. Moreau, Gord Gubitz, Aleksander Tkach, Dar Dowlatshahi, George Medvedev, Jennifer Mandzia, Aleksandra Pikula, Jai Shankar, Heather Williams, Herbert Manosalva, Muzaffar Siddiqui, Atif Zafar, Oje Imoukhuede, Gary Hunter, Stephen Phillips, Michael D. Hill, Alexandre Y. Poppe, Ayoola Ademola, Michel Shamy, Fouzi Bala, Tolulope T. Sajobi, Richard H. Swartz, Mohammed Almekhlafi, Bijoy K. Menon, Thalia S. Field

Bibliographic record

VenueStroke · 2024
Typearticle
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsRed Deer Regional HospitalCentre Hospitalier de l’Université de MontréalMedicine Hat Regional HospitalUniversity of British ColumbiaToronto Western HospitalLondon Health Sciences CentreWestern UniversityUniversité de SherbrookeQueen's UniversityOttawa HospitalUniversity of Alberta HospitalHamilton Health SciencesSt. Michael's HospitalSunnybrook Health Science CentreKelowna General HospitalUniversity of ManitobaQueen Elizabeth II Health Sciences CentreUniversity of SaskatchewanRoyal Columbian HospitalUniversité de MontréalGrey Nuns Community HospitalUniversity of AlbertaUniversity of Calgary
Fundersnot available
KeywordsMedicineTenecteplaseStroke (engine)CardiologyInternal medicineThrombolysisIschemic strokeEmergency medicineIschemiaMyocardial infarction

Abstract

fetched live from OpenAlex

BACKGROUND: Understanding sex differences in stroke care is important in reducing potential disparities. Our objective was to explore sex differences in workflow efficiency, treatment efficacy, and safety in the AcT trial (Alteplase Compared to Tenecteplase). METHODS: AcT was a multicenter, registry-linked randomized noninferiority trial comparing tenecteplase (0.25 mg/kg) with alteplase (0.9 mg/kg) in acute ischemic stroke within 4.5 hours of onset. In this post hoc analysis, baseline characteristics, workflow times, successful reperfusion (extended Thrombolysis in Cerebral Infarction score ≥2b), symptomatic intracerebral hemorrhage, 90-day functional independence (modified Rankin Scale score, 0-1), and 90-day mortality were compared by sex. Mixed-effects regression analysis was used adjusting for age, stroke severity, and occlusion site for outcomes. RESULTS: Of 1577 patients treated with intravenous thrombolysis (2019-2022), 755 (47.9%) were women. Women were older (median, 77 [68-86] years in women versus 70 [59-79] years in men) and had a higher proportion of severe strokes (National Institutes of Health Stroke Scale score >15; 32.4% versus 24.9%) and large vessel occlusions (28.7% versus 21.5%) compared with men. All workflow times were comparable between sexes. Women were less likely to achieve functional independence (31.7% versus 39.8%; unadjusted relative risk, 0.80 [95% CI, 0.70-0.91]) and had higher mortality (17.7% versus 13.3%; unadjusted relative risk, 1.33 [95% CI, 1.06-1.69]). Adjusted analysis showed no difference in outcomes between sexes. CONCLUSIONS: Differences in prognostic factors of age, stroke severity, and occlusion site largely accounted for higher functional dependence and mortality in women. No sex disparities were apparent in workflow quality indicators. Given the integration of the AcT trial into clinical practice, these results provide reassurance that no major sex biases are apparent in acute stroke management throughout participating Canadian centers. REGISTRATION: URL: https://www.clinicaltrials.gov; Unique identifier: NCT03889249.

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)0.002
metaresearch head score (Gemma)0.006
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 score0.002
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.009
GPT teacher head0.256
Teacher spread0.247 · 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

Citations9
Published2024
Admission routes2
Has abstractyes

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