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Record W2095590579 · doi:10.1109/ccece.2002.1013114

Quantitative MR cerebral blood flow using ARMA-based deconvolution: preliminary results

2003· article· en· W2095590579 on OpenAlexaff
Hong Lu, Michael R. Smith, Richard Frayne

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicAdvanced MRI Techniques and Applications
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsDeconvolutionAutoregressive–moving-average modelSingular value decompositionCerebral blood flowAutoregressive modelBlood flowComputer scienceArtificial intelligencePattern recognition (psychology)MathematicsSpeech recognitionAlgorithmMedicineCardiologyStatistics

Abstract

fetched live from OpenAlex

A system for determining quantitative cerebral blood flow (CBF) from dynamic susceptibility contrast (DSC) images was developed. The system uses a novel deconvolution method, based on autoregressive and moving-average (ARMA) processing, to extract CBF from the DSC images. The ARMA-based method is expected to he less sensitive to many factors compared to the more commonly used singular-value decomposition (SVD) method. In selected acute stroke patients, the SVD- and ARMA-derived CBF results were equivalent.

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)0.003
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.057
GPT teacher head0.356
Teacher spread0.299 · 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 designBench or experimental
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

Citations4
Published2003
Admission routes1
Has abstractyes

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