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Record W4321370783 · doi:10.1259/bjr.20221079

Accumulation of iodine or other similar K-edge equivalent element within renal cysts mimics enhancing masses at single-phase dual-energy CT

2023· article· en· W4321370783 on OpenAlexaff
Michael J. Magnetta, Nicola Schieda, P. J. Murphy, Frank H. Miller

Bibliographic record

VenueBritish Journal of Radiology · 2023
Typearticle
Languageen
FieldEngineering
TopicAdvanced X-ray and CT Imaging
Canadian institutionsOttawa HospitalUniversity of Ottawa
Fundersnot available
KeywordsIodinePhase (matter)Dual energyMaterials scienceElement (criminal law)K-edgeEnhanced Data Rates for GSM EvolutionNuclear medicineChemistryMedicinePhysicsPathologyOpticsComputer science

Abstract

fetched live from OpenAlex

OBJECTIVE: To describe instances of iodine, or other element with similar K-edge to iodine, accumulating in benign renal cysts and simulating solid renal masses (SRM) at single-phase contrast-enhanced (CE) dual-energy CT (DECT). METHODS: During the course of routine clinical practice, instances of benign renal cysts (reference standard true non-contrast enhanced CT [NCCT] homogeneous attenuation <10 HU and not enhancing, or MRI) simulating SRM at follow-up single-phase CE-DECT due to iodine (or other element) accumulation were documented in two institutions over a 3-month observation period in 2021. RESULTS: [2.8-20.9]). CONCLUSION: The accumulation of iodine, or other element with similar K-edge to iodine, in benign renal cysts could simulate enhancing renal masses at single-phase contrast-enhanced DECT.

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)0.000
metaresearch head score (Gemma)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.049
GPT teacher head0.310
Teacher spread0.261 · 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

Citations2
Published2023
Admission routes1
Has abstractyes

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