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Record W3139045742 · doi:10.1161/str.52.suppl_1.mp59

Abstract MP59: Retinal Vascular Metrics Predict Pial Collateral Status in Patients With Acute Ischemic Stroke

2021· article· en· W3139045742 on OpenAlexaff
Adnan Khan, Saadat Kamran, Patrick De Boever, Nele Gerrits, Maher Saqqur, Ioannis N. Petropoulos, Georgios Ponirakis, Naveed Akhtar, Ashfaq Shuaib, Rayaz A. Malik

Bibliographic record

VenueStroke · 2021
Typearticle
Languageen
FieldMedicine
TopicRetinal Imaging and Analysis
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMedicineRetinalCardiologyTortuosityInternal medicineCollateral circulationStroke (engine)Ophthalmology

Abstract

fetched live from OpenAlex

Background: The extent of the pial collateral circulation may determine outcomes in an acute ischemic stroke. Experimental studies suggest that retinal vessel metrics and geometric patterning may predict the pial collateral status. We have undertaken a translational study to quantify and relate retinal vascular metrics to the grade of pial collaterals in patients with acute ischemic stroke. Method: 35 patients admitted with acute stroke underwent computed tomography angiography (OCT) and were graded as having good (n=20)(47.55 ± 10.65 years) or poor (n=15) (48.93 ± 10.91 years) pial collaterals and compared to healthy controls (n=21)(44.26 ± 10.15 years). Retinal images were generated using OCT and central retinal artery equivalent (CRAE), central retinal vein equivalent (CRVE), artery-to-vein ratio (AVR), segmented fractal analysis and lacunarity, tortuosity index and fractal dimensions (capacity D 0 , information D 1 and correlation D 2 , curve asymmetry, singularity length and f-alfa-max using MONA software) were quantified. Results: Age ( p =0.709), BMI ( p =0.451), total cholesterol ( p =0.845), triglycerides ( p =0.679), LDL ( p =0.953), HDL ( p =0.361) and HbA 1c ( p =0.210) were comparable but the national institute of health stroke scale ( p =0.031) and modified Rankin Scale ( p =0.048) were higher in patients with poor compared to good collaterals. CRAE ( p =0.114), CRVE ( p =0.946), AVR ( p =0.114), lacunarity ( p =0.442), tortuosity index ( p =0.681), fractal analysis ( p =0.656), curve asymmetry ( p =0.619) and singularity length ( p =0.944) did not differ between patients with poor compared to good collaterals. However, fractal capacity D 0 (1.673 ± 0.029 vs 1.654 ± 0.025, p =0.042), fractal information D 1 (1.610 ± 0.027 vs 1.591 ± 0.024, p =0.036), fractal correlation D 2 (1.581± 0.028 vs 1.564 ± 0.024, p =0.060), and f alfa max (1.674 ± 0.027 vs 1.654 ± 0.025, p =0.030) were higher in patients with poor compared to good collaterals. Conclusion: This study shows differences in retinal vessel fractal dimensions between acute stroke patients with poor compared to good pial collaterals. This represents a non-invasive imaging method to define the pial collateral status and develop personalized intervention management strategies in acute ischemic 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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.012
Threshold uncertainty score0.743

Codex and Gemma teacher scores by category

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

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 head0.006
GPT teacher head0.241
Teacher spread0.235 · 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 teacher head, 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
Published2021
Admission routes1
Has abstractyes

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