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Incidentalomas Among Healthy Nephrology Fellow Volunteers at POCUS Workshops: A Case Series

2022· article· en· W4210772770 on OpenAlexvenueno aff
Ira Blau, Behdad Besharatian, Nathaniel Reisinger

Bibliographic record

VenuePOCUS Journal · 2022
Typearticle
Languageen
FieldMedicine
TopicRenal cell carcinoma treatment
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineNephrologyIncidence (geometry)IncidentalomaInternal medicineRadiologyGeneral surgery

Abstract

fetched live from OpenAlex

A radiographic incidental finding (sometimes called an incidentaloma) is defined as a structure that is unintentionally found during an exam for an unrelated indication. The increased use of routine abdominal imaging is associated with a rising incidence in incidentalomas of the kidney 1. In one meta-analysis, 75% of renal incidentalomas were benign 2. However, the overall prevalence of incidental carcinomas is low at 0.2% 3. With the growing uptake of POCUS, healthy volunteers for clinical demonstrations may find themselves with new findings despite a lack of symptoms 4. Having an incidentaloma discovered during the course of a nephrology POCUS workshop is a unique experience. Herein we report our experiences of having incidentalomas discovered during the course of POCUS demonstrations.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.430
Threshold uncertainty score1.000

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.018
GPT teacher head0.263
Teacher spread0.245 · 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.

Study designCase report
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

Citations1
Published2022
Admission routes1
Has abstractyes

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