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Record W4400877909 · doi:10.1161/strokeaha.124.046690

Interhospital Transfer for Endovascular Stroke Treatment in Canada: Results From the OPTIMISE Registry

2024· article· en· W4400877909 on OpenAlexaffabout
Aristeidis H. Katsanos, Alexandre Y. Poppe, Richard H. Swartz, Jennifer Mandzia, Luciana Catanese, Jai Shankar, Samuel Yip, Steve Verreault, George Medvedev, Ilavarasy Maran, C. LEGAULT, D. Ferguson, Brian Archer, Aditya Bharatha, David Volders, Michael Kelly, Federico Carpani, Aleksandra Pikula, Alexander Tkach, F. Moreau, Michel Beaudry, Ramana Appireddy, Aviraj S. Deshmukh, Mohammed Almekhlafi, Robert Fahed, Noreen Kamal, Bijoy K. Menon, Ashkan Shoamanesh, Heather Williams, Amy Yu, Manraj K. S. Heran, Michael D. Hill, Mukul Sharma, Karen Earl, Andrew M. Demchuk, Grant Stotts

Bibliographic record

VenueStroke · 2024
Typearticle
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsOttawa HospitalUniversity of CalgaryNOSM UniversityQueen's UniversityUniversity of SaskatchewanSunnybrook Health Science CentreCentre de Santé et de Services Sociaux de ChicoutimiUniversity of British ColumbiaSaint John Regional HospitalDalhousie UniversityMcGill University Health CentreUniversité du Québec à MontréalUniversité de SherbrookeTrillium Health CentreUniversity of ManitobaHôpital de l'Enfant-JésusToronto Western HospitalPopulation Health Research InstituteWestern UniversityKelowna General HospitalSt. Michael's HospitalCentre Hospitalier de l’Université de MontréalHealth Sciences NorthLondon Health Sciences CentreFraser Health
Fundersnot available
KeywordsMedicineStroke (engine)Endovascular treatmentEmergency medicineAcute strokeSurgeryInternal medicineAneurysmTissue plasminogen activator

Abstract

fetched live from OpenAlex

BACKGROUND: Interhospital transfer for patients with stroke due to large vessel occlusion for endovascular thrombectomy (EVT) has been associated with treatment delays. METHODS: We analyzed data from Optimizing Patient Treatment in Major Ischemic Stroke With EVT, a quality improvement registry to support EVT implementation in Canada. We assessed for unadjusted differences in baseline characteristics, time metrics, and procedural outcomes between patients with large vessel occlusion transferred for EVT and those directly admitted to an EVT-capable center. RESULTS: Between January 1, 2018, and December 31, 2021, a total of 6803 patients received EVT at 20 participating centers (median age, 73 years; 50% women; and 50% treated with intravenous thrombolysis). Patients transferred for EVT (n=3376) had lower rates of M2 occlusion (22% versus 27%) and higher rates of basilar occlusion (9% versus 5%) compared with those patients presenting directly at an EVT-capable center (n=3373). Door-to-needle times were shorter in patients receiving intravenous thrombolysis before transfer compared with those presenting directly to an EVT center (32 versus 36 minutes). Patients transferred for EVT had shorter door-to-arterial access times (37 versus 87 minutes) but longer last seen normal-to-arterial access times (322 versus 181 minutes) compared with those presenting directly to an EVT-capable center. No differences in arterial access-to-reperfusion times, successful reperfusion rates (85% versus 86%), or adverse periprocedural events were found between the 2 groups. Patients transferred to EVT centers had a similar likelihood for good functional outcome (modified Rankin Scale score, 0-2; 41% versus 43%; risk ratio, 0.95 [95% CI, 0.88-1.01]; adjusted risk ratio, 0.98 [95% CI, 0.91-1.05]) and a higher risk for all-cause mortality at 90 days (29% versus 25%; risk ratio, 1.15 [95% CI, 1.05-1.27]; adjusted risk ratio, 1.14 [95% CI, 1.03-1.28]) compared with patients presenting directly to an EVT center. CONCLUSIONS: Patients transferred for EVT experience significant delays from the time they were last seen normal to the initiation of EVT.

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)0.007
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 score0.025
Threshold uncertainty score0.180

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.008
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.002
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 head0.015
GPT teacher head0.239
Teacher spread0.224 · 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

Citations10
Published2024
Admission routes2
Has abstractyes

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