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Record W2763004239 · doi:10.1161/str.47.suppl_1.tp57

Abstract TP57: Assessing Acute Infarct Growth and Evolution Using Apparent Diffusion Coefficient and Quantitative R2 Imaging

2016· article· en· W2763004239 on OpenAlexaff
Rani Gupta Sah, Saad A. Khan, Ajay Mahajan, Nils D. Forkert, Adrian Tsang, Moiz Hafeez, Sana Tariq, Christopher D. d’Esterre, Phillip A Barber

Bibliographic record

VenueStroke · 2016
Typearticle
Languageen
FieldMedicine
TopicMRI in cancer diagnosis
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMedicineDiffusion MRIEffective diffusion coefficientMagnetic resonance imagingLesionStroke (engine)Neurovascular bundleIschemic strokeNuclear medicineRadiologyIschemiaInternal medicinePathology

Abstract

fetched live from OpenAlex

Introduction: Ischemic stroke is caused by cellular injury to the neurovascular unit. During the early stages of stroke onset, MR diffusion-weighted Imaging (DWI) provides sensitive detection of cytotoxic injury. However, measurement of free-water with quantitative R2 (qR2) relaxometry is generally ignored. Hypothesis: Inclusion of qR2 with DWI will better characterize the heterogeneity of acute ischemia and infarct growth. Methods: 36 ischemic stroke patients (age; 72 ± 17) were imaged acutely (baseline) and at 24h on a 3 T MR scanner. Apparent diffusion coefficient (ADC) and qR2 maps were calculated from DWI and multi-echo T2 data sets using ANTONIA software. Volumetric segmentation of qR2 lesions were performed using a threshold -12.5% of the mean contra lateral side value. ADC lesions were segmented using a threshold of 630x10^-6mm^2/s. Lesion overlap of qR2 and ADC segmented volumes was also calculated using DICE coefficient. Results: Time from stroke onset to MRI was 4:5 ± 0.16h, and recanalization was confirmed (10 ± 1.3h) in the acute period. Treatment was provided with tPA in 27/36 (75%) or endovascular therapy in 15/36 (41.7%) or both in 9/36 (25%). Ischemic lesions were identified in 31/36 (86%) patients based on ADC. ADC lesions were identified in 23/31 (74%) at baseline and 27/31 (87%) at 24h, QR2 lesions were observed in 14/27 (52 %) at baseline and 15/27 (56%) at 24h. The Figure shows ADC and qR2 volume changes over time. QR2 and ADC volume overlap was 10.7% at baseline and 19.9% at 24h. The percentage change in qR2 lesion was 189.2%, compared to ADC lesion growth of 90.50%. Lesion growth was more frequently demonstrated by qR2 than ADC (Fishers exact p<0.05). Conclusion: Acute ischemic lesion growth size was more frequently demonstrated by qR2 than by ADC. Acquisition of ADC alone underestimates severity of acute ischemia and infarct growth. Combining quantitative MRI using ADC and qR2 provide improved characterization of acute infarct evolution.

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)0.002
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 score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.027
GPT teacher head0.333
Teacher spread0.306 · 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
Published2016
Admission routes1
Has abstractyes

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