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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: Five Bosniak one renal cysts (12 ± 7 mm) in five patients changed nature on follow-up imaging simulating SRM at CE-DECT. At time of DECT, cyst attenuation on true NCCT (mean 91 ± 25 HU [Range 56–120]) was significantly higher compared to virtual NCCT (mean 11 ± 22 HU [-23–30], p = 0.003) and all five cysts showed internal iodine content on DECT iodine maps with concentration >1.9 mg ml −1 (mean 8.2 ± 7.6 mg ml −1 [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 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)0.000
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.389
Threshold uncertainty score0.706

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
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 head0.049
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 teacher head, 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

Citations2
Published2023
Admission routes1
Has abstractyes

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