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Record W4302774499 · doi:10.1097/aog.0000000000004956

Periurethral and Anterior Vaginal Wall Masses

2022· article· en· W4302774499 on OpenAlexaff
Natalie Jacox, Henry H. Yao, Richard Baverstock, Kiril Trpkov, Kevin Carlson

Bibliographic record

VenueObstetrics and Gynecology · 2022
Typearticle
Languageen
FieldMedicine
TopicUrinary and Genital Oncology Studies
Canadian institutionsUniversity of CalgaryUniversity of Alberta
Fundersnot available
KeywordsMedicineEtiologyMalignancySurgeryUrinary incontinenceInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: To describe the etiology and presenting symptoms of periurethral and anterior vaginal wall masses in a large series of patients in an academic institution. METHODS: A retrospective chart review of 126 patients presenting and undergoing treatment for periurethral and anterior vaginal wall masses between November 2001 and July 2021 was completed. Clinicopathologic data were extracted. Ethics approval was obtained. The primary objective of this study was to determine the etiology of these masses; secondary objectives included determining the rates of presenting symptoms, complications, resolution of stress urinary incontinence (SUI), and de novo SUI. RESULTS: The median age of patients was 42 years. The most common etiology was urethral diverticula (39.7%), followed by Skene gland cysts or abscesses (30.2%). The rate of malignancy was 1.6%, and the rate of infection was 21.4%. The most common presenting symptoms were sensation of mass (78.6%), dyspareunia (52.4%), and discharge (46.0%). The rate of surgical complications was 9.5%. Three patients had recurrence on follow-up, but there were no recurrent urethral diverticula after excision. The rate of de novo SUI was 5.6%. The rate of resolution of SUI was 67.6%, and all patients who had slings reported resolution of SUI. CONCLUSION: Urethral diverticula and Skene gland cysts or abscesses accounted for 70% of periurethral and anterior vaginal wall masses in this series. Treatment by complete excision is usually successful.

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)0.001
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 score0.530
Threshold uncertainty score0.350

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.017
GPT teacher head0.269
Teacher spread0.252 · 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

Citations4
Published2022
Admission routes1
Has abstractyes

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