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Record W4407995237 · doi:10.1097/ju.0000000000004490

Updates to Microhematuria: AUA/SUFU Guideline (2025)

2025· article· es· W4407995237 on OpenAlexaff
Daniel A. Barocas, Yair Lotan, Richard S. Matulewicz, Jay D. Raman, Mary E. Westerman, Erin Kirkby, Lauren J. Pak, Lesley Souter

Bibliographic record

VenueThe Journal of Urology · 2025
Typearticle
Languagees
FieldMedicine
TopicBladder and Urothelial Cancer Treatments
Canadian institutionsSmiths Detection (Canada)
Fundersnot available
KeywordsMedicineMicrohematuriaGuidelineIntensive care medicineInternal medicinePathology

Abstract

fetched live from OpenAlex

PURPOSE: In 2023 the AUA in collaboration with the Society for Urodynamics, Female Pelvic Medicine & Urogenital Reconstruction (SUFU) requested an Update Literature Review to incorporate new evidence generated since the 2020 publication of this Guideline. The resulting 2025 Microhematuria Guideline Amendment addresses updated recommendations to provide a clinical framework for the diagnosis, evaluation, and follow-up of microhematuria. MATERIALS AND METHODS: In 2024, this Guideline was reviewed via the AUA Update Literature Review process, which identified 82 studies for full-text review that were published between December 2019 and June 7, 2024. Of those 82 studies, 23 met inclusion criteria for qualitative synthesis. The subsequent amendment is based on data released since the initial 2020 publication of this Guideline. RESULTS: The Panel developed evidence- and consensus-based statements based on an updated review to provide guidance on evaluation and management of microhematuria. These updates are detailed herein. CONCLUSIONS: This update provides several new insights, including a revised risk stratification system, updated information regarding use of urine-based tumor markers and cytology, and new guidance on diagnosis and surveillance. This Guideline will require further review as the diagnostic and treatment options in this space continue to evolve.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.590
Threshold uncertainty score0.947

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.012
GPT teacher head0.321
Teacher spread0.309 · 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 designNot applicable
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

Citations42
Published2025
Admission routes1
Has abstractyes

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