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Record W2254500285 · doi:10.1161/str.46.suppl_1.wp12

Abstract W P12: Baseline Predictors of the Malignant Collateral Profile in IMS III

2015· article· en· W2254500285 on OpenAlexaff
David S. Liebeskind, Tudor G. Jovin, Bijoy K. Menon, Raul G. Nogueira, Osama O. Zaidat, Fabien Scalzo, Michael D. Hill, Andrew M. Demchuk, Janice Carrozzella, Rüdiger von Kummer, Pooja Khatri, Mayank Goyal, Firas Ali, Bernard Yan, Lydia D. Foster, Sharon D. Yeatts, Yuko Y. Palesch, Joseph P. Broderick, Thomas A. Tomsick, Albert J. Yoo

Bibliographic record

VenueStroke · 2015
Typearticle
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsHotchkiss Brain InstituteOntario Brain Institute
Fundersnot available
KeywordsMedicineCollateral circulationAngiographyUnivariate analysisLogistic regressionStroke (engine)Internal medicineRadiologyMultivariate analysisCardiology

Abstract

fetched live from OpenAlex

Background: Collateral circulation has repeatedly been cited as a decisive factor in successful angiographic and clinical outcomes after endovascular therapy, offering rational selection criteria. Identifying patients with a malignant collateral profile, portending poor outcome, would greatly enhance decision-making in acute stroke. We analyzed the IMS III dataset to delineate baseline predictors of poor angiographic collaterals. Methods: Collateral grade was prospectively evaluated by the angiography core lab in IMS III. Poor collaterals or a malignant collateral profile was defined as ASITN grade 0-1. Baseline clinical, laboratory and non-contrast CT variables were evaluated in univariate and multivariable logistic regression as predictors of the malignant collateral profile. Results: 278 patients (mean age 65.3±12.6 years, 54% women, median NIHSS 17 (IQR 13-20) had collateral grading assessed at angiography. Malignant collaterals (ASITN 0-1) were noted in 77/278 (28%). Univariate analyses revealed that only history of HTN (88.3 vs. 71.0%, p=0.004), CHF (17.6 vs.8.0%, p=0.040), admission DBP (89±24 vs. 81±17 (mm Hg, mean±SD), p=0.002), NIHSS>19 (40.3 vs. 28.4%, p=0.080), distal arterial occlusion location (p=0.002) and ASPECTS 0-4 (24.7 vs. 9.4%, p=0.002) were associated with malignant collaterals. Time from stroke onset to angiography was unrelated to collateral grade. Predictors in multivariable analyses included ASPECTS 0-4 (OR 3.72, 95%CI (1.60-8.66), p=0.002), HTN (OR 2.33, 95%CI (1.01-5.38), p=0.047), NIHSS>19 (OR 2.26, 95%CI (1.16-4.40), p=0.017), distal arterial occlusion (OR 2.08, 95%CI (1.43-3.04), p<0.001) and higher admission DBP (OR 1.02 per mm Hg, 95%CI (1.00-1.03), p=0.035). When ASPECTS 5-10, only NIHSS>19 (OR 2.81, 95%CI (1.35-5.86), p=0.006) and distal arterial occlusion (OR 2.58, 95%CI (1.70-3.92), p<0.001) predicted malignant collaterals. Conclusions: Elevated diastolic blood pressure, history of hypertension, NIHSS>19, lower ASPECTS and distal arterial occlusion are strong predictors of the malignant collateral profile. Future studies should validate and implement a concomitant risk score for triage of acute stroke patients.

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)0.003
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 score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
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 science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.001

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 head0.254
Teacher spread0.237 · 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".

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Citations0
Published2015
Admission routes1
Has abstractyes

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