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Record W4232060647 · doi:10.1161/str.43.suppl_1.a3820

Abstract 3820: Penumbra Imaging Collaborative Study (PICS): Utilization of Imaging for Patient Selection and Its Impact on Outcomes Following Penumbra System <sup>®</sup> Treatment

2012· article· en· W4232060647 on OpenAlexaff
Osama Zaidat, Sean Meagher, Michael Brant‐Zawadzki, Jeffrey Farkas, Reza S. Malek, B Crandall, Donald Frei, Ferdinand Hui, Michael J. Alexander, Brian W. Chong, Nazli Janjua, Darryn Shaff, Dileep R. Yavagal, Donald Heck, Tim Malisch, Aquilla S Turk, M. Hayakawa, László Miskolczi, Robert Tarr, Rafael Aguayo Ortiz, Alois Zauner, Richard Klucznik, Christopher Zylak, Albert J. Yoo, Elan Mualem, Arani Bose, Siu Po Sit

Bibliographic record

VenueStroke · 2012
Typearticle
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsCrandall University
Fundersnot available
KeywordsMedicinePenumbraTIMITriageModified Rankin ScaleStroke (engine)Perfusion scanningRadiologyThrombolysisAcute strokeMagnetic resonance imagingNuclear medicinePerfusionInternal medicineEmergency medicineIschemic strokeMyocardial infarctionTissue plasminogen activator

Abstract

fetched live from OpenAlex

Purpose: Various imaging approaches are utilized for evaluating acute stroke patients. Despite numerous studies, there is no clear evidence that one approach is superior to another for identifying which patients will benefit from mechanical thrombectomy (MT). The PICS registry was established to determine the imaging modalities being employed to triage acute stroke patients for MT and to assess their impact on patient functional outcome. Methods: Patients were enrolled across 35 centers. For each patient, the baseline imaging modality was recorded to determine the utilization rates of noncontrast CT (NCCT), CT perfusion (CT-P), and MRI diffusion weighted imaging (DWI). All patients subsequently treated by the Penumbra System per standard of care were followed for 3 months after the procedure to assess functional outcome using the modified Rankin Scale (mRS). Results: A total of 305 patients were enrolled in this registry, of which 267 had the requisite imaging modalities used for this analysis. Of these, 49.4% were females. Mean age was 66.6 ± 15.9 years; median NIHSS score was 17.0 (IQR 12-21). The median time from stroke onset to presentation was 2.3 hours, from stroke symptom onset to arterial puncture was 4.8 hours, and from arterial puncture to end of thromboaspiration was 73.0 minutes. Post-treatment evaluation revealed that 83.5% of patients were successfully recanalized to TIMI 2/3 (from TIMI 0/1) with 43.5% achieving a 90-day mRS of 0-2. All cause mortality was 19.9% with symptomatic intracranial hemorrhage reported in 4.5% of the patients. The principal imaging modalities used for patient triage were NCCT in 61.8%, CT-P in 27.3% and DWI in 10.9% of the study population. Of the patients selected for NCCT, 42.5% were functionally independent at 90 days, whereas for CT-P and DWI, the rates were 45.9% and 42.9%, respectively. There was no significant difference between groups for patient age, baseline NIHSS, time from onset to presentation, time from arterial puncture to end of thromboaspiration. The time from stroke symptom onset to groin puncture was significantly different in the DWI cohort (p<0.0001) with a median of 4.4 hrs for NCCT, 4.8 hrs for CTP, and 6.5 hrs for DWI. There was a significant difference in % of female patients with CTP 37.0%, DWI 48.3%, and NCCT 55.2% (p=0.034). Conclusion: The type of imaging modality utilized to triage acute ischemic stroke patients for MT varies widely across the US. Based on the present findings, the imaging approach appears to have little impact on patient functional outcome after MT.

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)0.003
metaresearch head score (Gemma)0.005
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 score0.003
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.019
GPT teacher head0.323
Teacher spread0.304 · 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
Published2012
Admission routes1
Has abstractyes

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