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Record W2739717578 · doi:10.1002/mrm.26858

In vivo phase imaging of human epiphyseal cartilage at 7 T

2017· article· en· W2739717578 on OpenAlexaff
Barbara Dymerska, K. Bohndorf, Paul Schennach, Alexander Rauscher, Siegfried Trattnig, Simon Robinson

Bibliographic record

VenueMagnetic Resonance in Medicine · 2017
Typearticle
Languageen
FieldMedicine
TopicAdvanced MRI Techniques and Applications
Canadian institutionsUniversity of British ColumbiaUniversity of British Columbia Hospital
FundersOesterreichische Nationalbank
KeywordsMagnetic resonance imagingCartilageKnee cartilageIn vivoNuclear magnetic resonanceQuantitative susceptibility mappingArticular cartilageGradient echoPhase imagingSusceptibility weighted imagingMagnetic susceptibilityBiomedical engineeringAnatomyMedicineNuclear medicineMaterials scienceRadiologyPathologyChemistryBiologyPhysicsOsteoarthritis

Abstract

fetched live from OpenAlex

PURPOSE: To assess the potential clinical utility of in vivo susceptibility-weighted imaging and quantitative susceptibility mapping of growth cartilage in the juvenile human knee at 7 T. METHODS: High-resolution gradient-echo images of the knees of six healthy children and adolescents aged 6 to 15 were acquired with a 28-channel coil at 7 T. Phase images from the coils were combined using a short echo-time reference scan method (COMPOSER). RESULTS: field appeared doubled in susceptibility-weighted imaging, but not quantitative susceptibility mapping. Veins and layers in the cartilage were visible in all children up to the age of 13. CONCLUSIONS: Phase imaging using susceptibility-weighted imaging and quantitative susceptibility mapping allows the in vivo visualization of veins and layers in human growth cartilage. Magn Reson Med 79:2149-2155, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
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 integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.028
GPT teacher head0.395
Teacher spread0.366 · 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

Citations13
Published2017
Admission routes1
Has abstractyes

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