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Record W2068998238 · doi:10.1002/ana.20638

Perfusion computed tomography: Prediction of final infarct extent and stroke outcome

2005· letter· en· W2068998238 on OpenAlexaboutno aff
Mark Parsons, Elizabeth Pepper, Virgil Chan, Sabbir Siddique, Siva Rajaratnam, Grant A. Bateman, Christopher Levi

Bibliographic record

VenueAnnals of Neurology · 2005
Typeletter
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineComputed tomographyStroke (engine)PerfusionPerfusion scanningCardiologyRadiologyOutcome (game theory)TomographyInternal medicineMathematics

Abstract

fetched live from OpenAlex

The Alberta Stroke Program Early Computed Tomography Score (ASPECTS) has not been previously applied to perfusion CT (CTP). Five raters assigned ASPECTS to baseline noncontrast CT (NCCT), CT angiography source images (CTA-SI), CTP source images (CTP-SI), and CTP maps of cerebral blood volume (CBV), cerebral blood flow (CBF), and mean transit time (MTT) from 37 consecutive patients with less than 6-hour anterior circulation ischemic stroke. Major reperfusion was identified on follow-up imaging. Mean baseline ASPECTS was compared with follow-up imaging ASPECTS. Rates of favorable outcome were compared for dichotomized baseline ASPECTS. In patients with major reperfusion, mean CBV and CTP-SI ASPECTS closely predicted final infarct ASPECTS. In patients without major reperfusion, mean CBF and MTT ASPECTS best predicted final infarct ASPECTS. There were significant increases in rates of favorable outcome for CTP-SI and CBV ASPECTS of greater than 6, versus less than or equal to 6, but not for other baseline CT modalities. ASPECTS applied to CTP is more accurate at identifying the extent of reversible and irreversible ischemia and at predicting final clinical outcome than NCCTor CTA-SI.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.183
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.068
GPT teacher head0.305
Teacher spread0.237 · 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.

Study designNot applicable
Domainnot available
GenreCommentary

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

Citations207
Published2005
Admission routes1
Has abstractyes

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