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Record W2089145076 · doi:10.4103/0971-3026.85370

Whole brain CT perfusion on a 320-slice CT scanner

2011· article· en· W2089145076 on OpenAlexaff
Jai Shankar, Cheemun Lum

Bibliographic record

VenueIndian journal of radiology and imaging - new series/Indian journal of radiology and imaging/Indian Journal of Radiology & Imaging · 2011
Typearticle
Languageen
FieldMedicine
TopicMRI in cancer diagnosis
Canadian institutionsOttawa HospitalQueen Elizabeth II Health Sciences Centre
Fundersnot available
KeywordsMedicinePerfusion scanningScannerPerfusionLesionRadiologyNuclear medicineBrain functionComputer sciencePathologyArtificial intelligenceNeuroscience

Abstract

fetched live from OpenAlex

Computed tomography perfusion (CTP) has been criticized for limited brain coverage. This may result in inadequate coverage of the lesion, inadequate arterial input function, or omission of the lesion within the target perfusion volume. The availability of 320-slice CT scanners offers whole brain coverage. This minimizes the chances of misregistration of lesions regardless of location, and makes the selection of the arterial input function easy. We present different clinical scenarios in which whole brain CTP is especially useful.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0160.003

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 head0.012
GPT teacher head0.256
Teacher spread0.244 · 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

Citations29
Published2011
Admission routes1
Has abstractyes

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Same venueIndian journal of radiology and imaging - new series/Indian journal of radiology and imaging/Indian Journal of Radiology & ImagingSame topicMRI in cancer diagnosisFrench-language works237,207