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Record W2162257829 · doi:10.1148/rg.305095222

Renal Pyramids: Focused Sonography of Normal and Pathologic Processes

2010· article· en· W2162257829 on OpenAlexaff
Alan Daneman, Oscar M. Navarro, Gino R. Somers, Arun Mohanta, José Jarrín, Jeffrey Traubici

Bibliographic record

VenueRadiographics · 2010
Typearticle
Languageen
FieldMedicine
TopicPediatric Urology and Nephrology Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsEchogenicityMedicineRadiologyPathologyUltrasound

Abstract

fetched live from OpenAlex

In neonates and children, sonographic examinations of the renal pyramids may depict a spectrum of unique changes in echogenicity due to the effects of physiologic processes or a wide variety of pathologic processes that may affect the collecting ducts or interstitium of the pyramids. Focused sonographic evaluation of the pyramids with high-frequency transducers produces the most detailed images of the pyramids, revealing some appearances not previously reported, to the authors' knowledge. The authors highlight the clinical settings in which they have documented detailed changes in the echogenicity of the pyramids. The patterns of altered echogenicity alone may reflect a specific cause but in many instances are nonspecific, with clinical and biochemical correlation required to establish a more precise diagnosis. However, there is a lack of histologic data to completely explain the mechanism of many of these changes in echogenicity in all of the processes. As the authors have expanded their use of the focused sonographic technique, they have been able to depict altered echogenicity in the pyramids in greater numbers of children in whom an explanation for the changes is not always immediately apparent; for now, the cause must be considered idiopathic. More work is required to expand the use of this focused technique together with clinical, biochemical, and histologic correlation in an attempt to offer more complete explanations for the changes in echogenicity of the pyramids.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.576

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.002
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 head0.010
GPT teacher head0.237
Teacher spread0.227 · 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 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

Citations53
Published2010
Admission routes1
Has abstractyes

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