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Periurethral and Anterior Vaginal Wall Masses: Etiology, Presentation, and Treatment Outcomes

2023· article· en· W4323073692 on OpenAlexaff
Natalie Jacox, Henry H. Yao, Richard Baverstock, Kiril Trpkov, Kevin Carlson

Bibliographic record

VenueObstetrical & Gynecological Survey · 2023
Typearticle
Languageen
FieldMedicine
TopicUrinary and Genital Oncology Studies
Canadian institutionsCalgary Laboratory ServicesAlberta Hospital EdmontonUniversity of CalgaryUniversity of Alberta
Fundersnot available
KeywordsDysuriaMedicineEtiologyUrinary systemLower urinary tract symptomsBody mass indexConcomitantPelvic painSurgeryInternal medicine

Abstract

fetched live from OpenAlex

ABSTRACT Periurethral and anterior vaginal masses are relatively common with an estimated prevalence of 3% to 4% and 1%, respectively. These masses often cause diagnostic challenges due to nonspecific and overlapping symptoms. Urinary symptoms include frequency, urgency, and dysuria; vaginal discharge, urinary tract infection (UTI), dyspareunia, bleeding, postvoid dribbling, pelvic and urethral pain, and palpable mass can also present. Familiarity with differentiation of masses is important to make timely diagnoses and to expedite treatment without delays. This study aimed to describe the etiology of periurethral and anterior vaginal wall masses in a large series, as well as report the clinical presentations to determine whether the differential diagnosis can be narrowed based on presenting symptoms. The study consisted of a retrospective chart review, based on charts of patients from November 2001 to July 2021 at a tertiary referral center. Analyzed data included age, body mass index, operative findings, operative time, concomitant procedures, pathologic diagnosis, catheterization time, complications, length of stay, symptoms on follow-up, and follow-up duration. Describing the etiology of periurethral and anterior vaginal wall masses was the primary objective of the study (including underlying etiology distribution), followed by the secondary objective of describing rates of other clinical characteristics. A systematic search of literature was performed using various databases: MEDLINE (Ovid), Cochran Central Register of Controlled Trials (Wiley Online Library), EMBASE (Elsevier), and Scopus. The study included a total of 126 patients, all with at least 1 symptom exhibited. The most common presenting symptoms were palpable mass, dyspareunia, and urinary tract symptoms (dysuria, stress urinary incontinence [SUI], and UTI). There was an infection rate of 21.4% and a malignancy rate of 1.6%. Each patient underwent surgical treatment, including urethral repair, periurethral cyst excision, urethral diverticulectomy, and lesion excision. A total of 29.4% of patients reported SUI preoperatively. Postoperatively, 105 patients reported no incontinence (83.3%), 15 reported SUI (11.9%), 5 reported mixed urinary incontinence (4%), and 1 presented with overflow incontinence (0.8%). Limitations of the study include the prospective collection of data but its retrospective analysis, thereby limiting findings according to the accuracy and completeness of the medical records. In addition, there was an absence of patient-reported outcome measures when SUI or other symptoms were assessed, and the authors recognize a need for future prospective studies to include such outcome measures. This large series evaluation of periurethral and anterior vaginal wall masses is the largest of its kind, as the previous study included only 79 patients with periurethral masses, 96% of whom had definitive pathological diagnoses and surgical treatment. This study provides a broad view into the etiological spectrum underlying periurethral and anterior vaginal masses. Malignancy is rare. Infectious pathology is common and found in 21.4% of patients, whereas urethral diverticulum was the most common pathology (40% of cases). Finally, this study indicated high success rates for surgical excisions, as there were no recurrences among diverticula patients in the study. For Skene’s gland cyst excisions, a recurrence rate of less than 1% was noted.

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)0.003
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 score0.008
Threshold uncertainty score0.571

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.003
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 integrity0.0000.000
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 head0.082
GPT teacher head0.359
Teacher spread0.277 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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