MétaCan
Menu
← Back to cohort
Record W2241015786 · doi:10.1161/str.43.suppl_1.a98

Abstract 98: Higher Volume Endovascular Stroke Centers Have Decreased Times to Treatment

2012· article· en· W2241015786 on OpenAlexaff
Rishi Gupta, Anat Horev, Thanh N. Nguyen, Raphael Y. Gershon, Dheeraj Gandhi, Dolora Wisco, Brenda Miller, Ashis Tayal, Bryan Ludwig, Muhammad Shazam Hussain, John B. Terry, Tudor G. Jovin, Samir Belagaje, Carolyn Cronin, Melissa Tian, Aaron Anderson, Michael Frankel, Kevin N. Sheth, David S. Liebeskind, Raul G. Nogueira

Bibliographic record

VenueStroke · 2012
Typearticle
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsMiller Group (Canada)
Fundersnot available
KeywordsMedicineStroke (engine)GroinUnivariate analysisSurgeryRadiologyInternal medicineMultivariate analysis

Abstract

fetched live from OpenAlex

Background: Currently, no metric exists for door to arterial puncture time for endovascular treatment in acute ischemic stroke. The aim of this study was to determine the timings of each step of endovascular stroke intervention stratified by the volume of procedures at each center. Methods: We retrospectively reviewed patients from seven institutions undergoing endovascular reperfusion therapies for acute ischemic stroke. Patients with anterior circulation strokes treated less than 8 hours from symptoms onset were included. Demographic, radiographic, angiographic and clinical outcomes were collected. The time interval at each milestone from CT acquisition to reperfusion was recorded. Successful reperfusion was defined as achieving a TICI 2B or 3 score as graded by the operator. Symptomatic hemorrhage was defined as a parenchymal hemorrhage type 2 as defined by ECASScriteria. Centers that performed more than 50 intra-arterial stroke interventions annually were considered high volume (HV) centers. A univariate analysis was performed with the Fisher’s exact test for categorical variables and students t-test for continuous variables to compare HV to lower volume (LV) centers. Variables with a p-value < 0.20 were placed in a binary logistic regression model to determine if there were differences in time to treatment between the two groups. Results: A total of 338 patients with a mean age of 67±14 years and mean NIHSS of 18±5 were included. The mean time from CT imaging to groin puncture was 108±73 minutes. The mean time from groin puncture to the placement of a microcatheter in the thrombus was 41±21 minutes and total procedure time 104±55 minutes. There were no differences in demographics, site of vascular occlusion and hemorrhage rates between high volume and lower volume centers. In univariate analysis, HV centers had a lower time from CT imaging to groin puncture (89±57 minutes vs. 154±84 minutes, p<0.001), procedure time (93±46 minutes vs. 129±65 minutes, p<0.001), final infarct volume (79±82 cm 3 vs. 94±106 cm 3 , p<0.03) and higher reperfusion rates (73% vs. 59%, p<0.01). In binary logistic regression modeling HV centers were found to have a shorter CT acquisition to arterial puncture time [OR 0.991, 95%CI (0.986-0.996), p<0.001] and higher reperfusion rates [OR 1.79, 95% CI (1.04-3.12), p<0.03]. Conclusions: Currently there is variability in the time from CT to arterial puncture and total procedure time across institutions, but HV centers appear to have lower times to treatment. Further study is required to determine how to reduce times to treatment and develop a metric for centers to target.

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)0.007
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 score0.010
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0100.001

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 head0.017
GPT teacher head0.259
Teacher spread0.243 · 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

Citations0
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueStroke→Same topicAcute Ischemic Stroke Management→French-language works237,207→