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Record W2011390521 · doi:10.1159/000094856

Does Clinical-CT ‘Mismatch’ Predict Early Response to Treatment with Recombinant Tissue Plasminogen Activator?

2006· article· en· W2011390521 on OpenAlexaboutno aff
John Y. Choi, Jennifer Pary, Andrei V. Alexandrov, Carlos A. Molina, Zsolt Garami, Marc Malkoff, Marta Rubiera, Hashem Shaltoni, Lemuel A. Moyé, James C. Grotta

Bibliographic record

VenueCerebrovascular Diseases · 2006
Typearticle
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsnot available
FundersNational Institute of Neurological Disorders and StrokeNational Institutes of Health
KeywordsMedicineMiddle cerebral arteryTranscranial DopplerStroke (engine)Internal medicineCardiologyPlasminogen activatorT-plasminogen activatorAcute strokeTissue plasminogen activatorNuclear medicineIschemia

Abstract

fetched live from OpenAlex

BACKGROUND: We hypothesized that patients with clinically severe strokes but less severe early ischemic changes on brain CT (i.e., clinical-CT mismatch) may respond better to intravenous recombinant tissue plasminogen activator (i.v. rt-PA) within 3 h of symptom onset. METHODS: In this secondary analysis of the CLOTBUST data, patients with middle cerebral artery occlusions on transcranial Doppler (TCD) were treated with i.v. rt-PA. Alberta Stroke Program Early CT Scores were obtained with raters blinded to the NIH Stroke Scale and TCD results. Two mismatch criteria and three criteria of response to therapy were explored. RESULTS: Of 126 patients, 67% had a mismatch type 1 and 74% had a mismatch type 2. The presence of clinical-CT mismatch by either definition did not correlate with any of the three criteria of response to rt-PA. Recanalization was a strong determinant of response, whether or not mismatch was present. CONCLUSIONS: Mismatch between severity of neurological deficit and CT findings is common but does not predict response to rt-PA therapy given within 3 h.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.331
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
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.011
GPT teacher head0.268
Teacher spread0.257 · 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 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
Published2006
Admission routes1
Has abstractyes

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