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Record W2071472876 · doi:10.3892/etm.2012.805

Factors affecting pre- and post-stenting computed tomography perfusion in patients with middle cerebral artery stenosis

2012· article· en· W2071472876 on OpenAlexaboutno aff
Hui Qu, Jinli Li, Xingquan Zhao, Kehui Dong

Bibliographic record

VenueExperimental and Therapeutic Medicine · 2012
Typearticle
Languageen
FieldMedicine
TopicCerebrovascular and Carotid Artery Diseases
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineStenosisMiddle cerebral arteryRadiologyStroke (engine)Perfusion scanningCollateral circulationPerfusionComputed tomography angiographyInternal medicineCardiologyComputed tomographyIschemia

Abstract

fetched live from OpenAlex

The aim of this study was to investigate the factors affecting pre- and post-stenting head computed tomography perfusion (CTP) in patients with middle cerebral artery stenosis. A total of 25 patients with severe middle cerebral artery stenosis were enrolled. CTP was performed prior to and following stenting. Scores were allocated to the time-to-peak (TTP) parameter of CTP using the Alberta stroke program early computed tomography scoring (ASPECTS) scale. The factors possibly affecting pre- and post-stenting CTP were analyzed. All the patients exhibited markedly prolonged TTP on the affected side prior to stenting, compared with the healthy side. Following surgery, the TTP was improved in all patients. The preoperative ASPECTS score was negatively correlated with the degree of middle cerebral artery stenosis with a correlation coefficient of -5.78. The preoperative vascular stenosis rate was positively correlated with the improvement degree of the ASPECTS score with a correlation coefficient of 1.137 (P=0.001). TTP is a sensitive parameter for evaluating the effect of stenting on middle cerebral artery stenosis. TTP prior to and following stenting may be quantitatively assessed using the ASPECTS scale. Patients with serious stenosis and/or good collateral circulation are able to benefit more from stenting.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.559

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 integrity0.0000.000
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 head0.017
GPT teacher head0.240
Teacher spread0.223 · 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

Citations3
Published2012
Admission routes1
Has abstractyes

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