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Record W1522648646 · doi:10.2147/tcrm.s2812

Early ischemic CT changes before thrombolysis: The influence of age and diabetes mellitus

2008· article· en· W1522648646 on OpenAlexaboutno aff
Lars Thomassen

Bibliographic record

VenueTherapeutics and Clinical Risk Management · 2008
Typearticle
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsnot available
FundersNational Institute of Neurological Disorders and Stroke
KeywordsMedicineDiabetes mellitusModified Rankin ScaleThrombolysisStroke (engine)Internal medicineMiddle cerebral arteryPediatricsIschemic strokeCardiologyPhysical therapyIschemiaMyocardial infarction

Abstract

fetched live from OpenAlex

OBJECTIVES: The significance of early ischemic changes (EIC) on computed tomography (CT) within 3 hours after stroke onset remains controversial. The semi-quantitative Alberta Stroke Program Early CT Score (ASPECTS) is found to have prognostic value in early stroke. This study assesses factors associated with the presence of EIC and the relation between EIC and clinical outcome. MATERIALS AND METHODS: CT scans from 61 consecutive patients receiving thrombolytic therapy were reviewed by 3 experienced stroke neurologists, assessing EIC (ASPECTS) and vascular signs (hyperdense middle cerebral artery stem and/or branches). Short-term outcome was assessed with the National Institute of Health Stroke Scale at 24 hours and long-term outcome with the modified Rankin Scale score after 3 months. RESULTS: The prevalence of EIC was 54% and the agreement between assessors was good (kappa 0.52-0.67). EIC was independently associated with younger age and absence of diabetes mellitus. Neither EIC nor vascular signs were associated with 3-months outcome. CONCLUSIONS: ASPECTS is as simple, systematic approach to assessing EIC, and the inter-observer agreement is good. Patient age and diabetes mellitus influence the presence of EIC.

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)0.001
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 score0.274
Threshold uncertainty score0.507

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.029
GPT teacher head0.306
Teacher spread0.277 · 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".

Quick stats

Citations8
Published2008
Admission routes1
Has abstractyes

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