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Record W4403083761 · doi:10.1177/10668969241286069

Nephrogenic Adenoma: A Pitfall on Frozen Section of Urethral Strictures

2024· article· en· W4403083761 on OpenAlexaff
Alexander Oberc, Christopher Sherman, Michelle R. Downes

Bibliographic record

VenueInternational Journal of Surgical Pathology · 2024
Typearticle
Languageen
FieldMedicine
TopicUrological Disorders and Treatments
Canadian institutionsHealth Sciences CentreSunnybrook Health Science CentreUniversity of Toronto
Fundersnot available
KeywordsMedicineFrozen section procedureMalignancyDysplasiaAdenomaSurgical pathologyRadiologySurgeryPathology

Abstract

fetched live from OpenAlex

Urethral strictures are a common cause of urinary obstruction which can be treated with surgical resection. Frozen sections are rare and pose a diagnostic challenge to pathologists due to the presence of benign lesions such as nephrogenic adenoma. We retrospectively examined all specimens of urethral stricture resections submitted to pathology at our institution from 2012 to 2022 (n = 258). Final pathology reports were searched to identify patients with dysplasia, carcinoma, or nephrogenic adenoma. When available, frozen section reports were also examined and compared to the final report, and additional clinical history and microscopic images were collected for patients with nephrogenic adenoma. Nephrogenic adenoma was identified in 3.8% (10/258) of urethral stricture resections. Dysplasia was identified in one patient who underwent two separate resections, and squamous cell carcinoma was found in one resection. Intraoperative frozen section was requested in 3.4% of resections (9/258). In two resections, an initial diagnosis of squamous cell carcinoma was initially favoured, however when reviewed with a genitourinary pathologist the diagnosis was changed to "reactive process" with a final diagnosis of nephrogenic adenoma. Nephrogenic adenoma can be challenging on frozen section due to variable architectural patterns, inflammation, and reactive changes. While urethral strictures are relatively common, their assessment by frozen section is rare and pathologists may lack familiarity with the variable morphology of benign entities that can be seen on frozen section resulting in their misinterpretation. We highlight this potential diagnostic pitfall and demonstrate the value of a second opinion prior to definitive frozen section diagnosis of malignancy.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.345
Threshold uncertainty score0.427

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 head0.016
GPT teacher head0.312
Teacher spread0.296 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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