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Record W4206484071 · doi:10.1017/cjn.2021.348

P.068 Discrepancy between post-treatment infarct volume and 90-day outcome in ischemic stroke: A validation study in the ESCAPE-NA1 randomized controlled trial

2021· article· en· W4206484071 on OpenAlexaffvenue
A Ganesh, JM Ospel, BK Menon, AM Demchuk, RG Nogueira, McTaggart Ra, AY Poppe, MA Almekhlafi, R Hanel, Götz Thomalla, Staffan Holmin, Volker Puetz, B Van Adel, JW Tarpley, Michael Tymianski, MD Hill, Manoj Goyal

Bibliographic record

VenueCanadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques · 2021
Typearticle
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsSystems, Applications & Products in Data Processing (Canada)Toronto Public HealthCalgary Laboratory Services
Fundersnot available
KeywordsMedicinePercentileStroke (engine)Stepwise regressionLogistic regressionRandomized controlled trialModified Rankin ScaleAdverse effectEndovascular treatmentInternal medicineIschemic strokeSurgeryAneurysmStatisticsIschemia

Abstract

fetched live from OpenAlex

Background: Some patients do poorly despite small infarcts after endovascular therapy(EVT) whilst others with large infarcts do well. We validated exploratory findings from the ESCAPE trial regarding factors associated with such discrepancies, in the ESCAPE-NA1 trial(NCT02930018). Methods: We identified “discrepant cases” with modified Rankin Scale(mRS)≥3 despite small follow-up infarct volume(FIV≤25th-percentile) on 24-hour CT/MRI or mRS≤2 despite large FIV(volume≥75th-percentile). We compared area-under-the-curve(AUC) of pre-specified logistic models containing (a)pre-treatment factors(age/cancer/vascular risk-factors) and (b)treatment-related/post-treatment factors(serious adverse events/SAEs) in identifying small-FIV/mRS≥3 and large-FIV/mRS≤2, with stepwise regression-derived models. Results: Among 1,091 patients, 42/287(14.6%) with FIV≤7mL(25th-percentile) had mRS≥3; 65/275(23.6%) with FIV≥92mL(75th-percentile) had mRS≤2. Pre-specified pre-treatment factors(age/cancer/vascular risk-factors) were associated with FIV≤7mL/mRS≥3; stepwise models selected similar variables(similar AUCs:0.92-0.93,p=0.42). SAEs(infarct-in-new-territory/recurrent stroke/pneumonia/heart failure) were strongly associated with FIV≤7mL/mRS≥3; stepwise models also identified onset-to-needle time and hemoglobin(24-hours) as treatment-related/post-treatment factors(similar AUCs:0.92-0.94,p=0.14). Younger age was associated with FIV≥92mL/mRS≤2; stepwise models also selected diabetes absence and baseline hemoglobin(similar AUCs:0.76-0.77,p=0.82). Absence of SAEs(stroke progression/pneumonia/intracerebral hemorrhage) was strongly associated with FIV≥92mL/mRS≤2; stepwise models also identified 24-hour hemoglobin, glucose, and BP(similar AUCs:0.79-0.80,p=0.030). Conclusions: FIV-mRS discrepancies are associated with pre-treatment factors like age/comorbidities; and post-treatment complications related to stroke evolution, secondary prevention, and post-acute care quality. Optimizing thrombolysis speed, BP, glucose, and hemoglobin are modifiable factors meriting further study.

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)0.047
metaresearch head score (Gemma)0.063
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.047
Threshold uncertainty score0.250

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0470.063
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.005
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0020.002
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 head0.035
GPT teacher head0.304
Teacher spread0.270 · 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 designRandomized trial
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
Published2021
Admission routes2
Has abstractyes

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