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Record W4399053215 · doi:10.1111/jon.13208

Lower admission stroke severity is associated with good collateral status in distal medium vessel occlusion stroke

2024· article· en· W4399053215 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Neuroimaging · 2024
Typearticle
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsSt. Michael's Hospital
Fundersnot available
KeywordsMedicineStroke (engine)Internal medicineOdds ratioCardiologyMiddle cerebral arteryConfidence intervalAtrial fibrillationDiabetes mellitusModified Rankin ScaleLogistic regressionIschemic strokeIschemia

Abstract

fetched live from OpenAlex

BACKGROUND AND PURPOSE: Distal medium vessel occlusions (DMVOs) are a significant contributor to acute ischemic stroke (AIS), with collateral status (CS) playing a pivotal role in modulating ischemic damage progression. We aimed to explore baseline characteristics associated with CS in AIS-DMVO. METHODS: This retrospective analysis of a prospectively collected database enrolled 130 AIS-DMVO patients from two comprehensive stroke centers. Baseline characteristics, including patient demographics, admission National Institutes of Health Stroke Scale (NIHSS) score, admission Los Angeles Motor Scale (LAMS) score, and co-morbidities, including hypertension, hyperlipidemia, diabetes, coronary artery disease, atrial fibrillation, and history of transient ischemic attack or stroke, were collected. The analysis was dichotomized to good CS, reflected by hypoperfusion index ratio (HIR) <.3, versus poor CS, reflected by HIR ≥.3. RESULTS: Good CS was observed in 34% of the patients. As to the occluded location, 43.8% occurred in proximal M2, 16.9% in mid M2, 35.4% in more distal middle cerebral artery, and 3.8% in distal anterior cerebral artery. In multivariate logistic analysis, a lower NIHSS score and a lower LAMS score were both independently associated with a good CS (odds ratio [OR]: 0.88, 95% confidence interval [CI]: 0.82-0.95, p < .001 and OR: 0.77, 95% CI: 0.62-0.96, p = .018, respectively). Patients with poor CS were more likely to manifest as moderate to severe stroke (29.1% vs. 4.5%, p < .001), while patients with good CS had a significantly higher chance of having a minor stroke clinically (40.9% vs. 12.8%, p < .001). CONCLUSIONS: CS remains an important determinant in the severity of AIS-DMVO. Collateral enhancement strategies may be a worthwhile pursuit in AIS-DMVO patients with more severe initial stroke presentation, which can be swiftly identified by the concise LAMS and serves as a proxy for underlying poor CS.

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.

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 score0.283
Threshold uncertainty score0.735

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.011
GPT teacher head0.270
Teacher spread0.258 · 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