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Record W1974710333 · doi:10.1002/jmri.21617

Identification of calcification with MRI using susceptibility‐weighted imaging: A case study

2008· article· en· W1974710333 on OpenAlex
Zhen Wu, Sandeep Mittal, Karl Kish, Yingjian Yu, Jiani Hu, E. Mark Haacke

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Magnetic Resonance Imaging · 2008
Typearticle
Languageen
FieldMedicine
TopicMedical Imaging and Pathology Studies
Canadian institutionsMcMaster University
FundersNational Heart, Lung, and Blood InstituteNational Institutes of Health
KeywordsSusceptibility weighted imagingCalcificationMedicineRadiologyT2 weightedMagnetic resonance imagingComputed tomographyNuclear medicine

Abstract

fetched live from OpenAlex

Susceptibility weighted imaging (SWI) is a new MRI technique that can identify calcification by using phase images. We present a single case with a partially calcified oligodendroglioma, multiple calcified cysticercosis lesions, and multiple physiologic calcifications in the same patient. SWI phase images and computed tomography (CT) images are compared. SWI phase images showed the same calcified lesions as shown on CT and sometimes some new calcifications. Our conclusion is that SWI filtered phase images can identify calcifications as well as CT in this case.

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.

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)0.001
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 score0.203
Threshold uncertainty score0.531

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.034
GPT teacher head0.323
Teacher spread0.289 · 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