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Record W2991386161 · doi:10.1007/s11239-019-01979-7

Modified diffusion-weighted imaging-Alberta Stroke Program Early Computed Tomography Score including deep white matter lesions predicts symptomatic intracerebral hemorrhage following intravenous thrombolysis

2019· article· en· W2991386161 on OpenAlexaboutno aff
Kõji Tanaka, Shoji Matsumoto, Konosuke Furuta, Takeshi Yamada, Sukehisa Nagano, Kei‐ichiro Takase, Taketo Hatano, Ryo Yamasaki, Jun‐ichi Kira

Bibliographic record

VenueJournal of Thrombosis and Thrombolysis · 2019
Typearticle
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsnot available
FundersJapan Society for the Promotion of ScienceCenter for Clinical and Translational Research
KeywordsMedicineIntracerebral hemorrhageThrombolysisWhite matterStroke (engine)Computed tomographyRadiologyDiffusion MRINeurologyNeuroimagingInternal medicineCardiologyMagnetic resonance imagingNuclear medicineSubarachnoid hemorrhage

Abstract

fetched live from OpenAlex

The Alberta Stroke Program Early Computed Tomography Score (ASPECTS) is widely used for the assessment of early ischemic changes (EICs) before thrombolysis. However, for symptomatic intracerebral hemorrhage (sICH) following intravenous recombinant tissue plasminogen activator (rt-PA), the prediction abilities of CT-ASPECTS, diffusion-weighted imaging (DWI)-ASPECTS, and DWI-ASPECTS including EICs in deep white matter (DWI-ASPECTS + W) are unclear. We investigated associations between each score and sICH following intravenous rt-PA. Data from consecutive patients who received intravenous rt-PA for acute ischemic stroke from 2005 to 2015 in four hospitals were retrospectively screened. We included data from patients who had undergone both CT and magnetic resonance imaging before thrombolysis and without evidence of posterior circulation stroke. We analyzed the ability of CT-ASPECTS, DWI-ASPECTS, and DWI-ASPECTS + W to predict sICH, accompanied by an increase in the National Institutes of Health Stroke Scale (NIHSS) score of ≥ 4 within the initial 36 h. Of 455 patients (273 men, median 75 years old), sICH occurred in 15 patients (3.3%). Receiver operating characteristics curve analysis showed that the optimal cut-offs of CT-ASPECTS, DWI-ASPECTS, and DWI-ASPECTS + W for predicting sICH were ≤ 9 (sensitivity 60.0%, specificity 59.8%, c-statistic 0.625), ≤ 6 (sensitivity 53.3%, specificity 80.9%, c-statistic 0.718), and ≤ 8 (sensitivity 86.7%, specificity 55.9%, c-statistic 0.756), respectively. A DWI-ASPECTS + W of ≤ 8 was independently associated with sICH (odds ratio 5.21, 95% confidence interval 1.30-35.31) after adjustment for pretreatment with antithrombotic agents, pretreatment NIHSS score, and large artery occlusions. DWI-ASPECTS + W predicted sICH in patients with acute anterior circulation stroke receiving intravenous rt-PA.

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)0.004
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 score0.007
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.013
GPT teacher head0.257
Teacher spread0.243 · 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".

Quick stats

Citations9
Published2019
Admission routes1
Has abstractyes

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