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Record W4404705835 · doi:10.1002/uog.29128

Prediction of vesicouterine adhesions by transvaginal sonographic sliding sign technique: validation study

2024· article· en· W4404705835 on OpenAlexaff
Nan Min, J. van Keizerswaard, Remco Visser, Nicole B. Burger, Jasmijn Rake, Johanna W. M. Aarts, T. Van den Bosch, Mathew Leonardi, Judith A.F. Huirne, Robert A. de Leeuw

Bibliographic record

VenueUltrasound in Obstetrics and Gynecology · 2024
Typearticle
Languageen
FieldMedicine
TopicUreteral procedures and complications
Canadian institutionsMcMaster University
FundersAmsterdam University Medical Centers
KeywordsSign (mathematics)RadiologyMedicineMathematics

Abstract

fetched live from OpenAlex

OBJECTIVE: Adhesions between the uterus, bladder and anterior abdominal wall are associated with clinical sequelae, including chronic pelvic pain and dyspareunia, and can also yield complications during surgery. The transvaginal sonographic (TVS) sliding bladder sign is a minimally invasive diagnostic tool to evaluate the presence of vesicouterine adhesions. This study aimed to determine the predictive value and intra- and interobserver variation of the TVS sliding bladder sign in the assessment of vesicouterine adhesions. METHODS: This was a prospective observational double-blind diagnostic accuracy study conducted at the Amsterdam University Medical Center. Patients scheduled for gynecological laparoscopic surgery for a benign disorder between January 2020 and December 2022 were included consecutively. All patients underwent preoperative TVS, including a dynamic sliding bladder sign examination in our outpatient clinic. Videoclips of the TVS scans were stored for offline assessment and used as an index test. The recordings of both TVS and laparoscopy were evaluated for diagnostic characteristics of vesicouterine adhesions by independent assessors, who were blinded to the clinical situation in addition to the laparoscopic findings when assessing recordings of TVS and vice versa. The presence of adhesions on laparoscopy was used as the reference standard. The positive predictive value (PPV), negative predictive value (NPV), specificity and sensitivity of the sliding bladder sign were calculated. In addition, inter- and intraobserver variability of the sliding bladder sign on TVS were assessed. RESULTS: Of 116 included women, 57 had a negative sliding bladder sign on TVS, while on laparoscopy, 51 women had mild and 28 had severe vesicouterine adhesions. A negative sliding bladder sign had a PPV of 94.7% (95% CI, 88.9-100%) for the presence of any vesicouterine adhesions, and a positive sliding bladder sign had a specificity of 91.9% (95% CI, 83.1-100%). For severe adhesions, the negative sliding bladder sign had a sensitivity of 89.3% (95% CI, 77.8-100%) and a positive sliding bladder sign had a NPV of 94.9% (95% CI, 89.3-100%). When using Cohen's kappa coefficient, inter- and intraobserver agreement between assessors was good. CONCLUSIONS: Sliding bladder sign evaluation using TVS is a reliable diagnostic tool for the prediction of vesicouterine adhesions on laparoscopy. A negative sliding bladder sign indicates the presence of vesicouterine adhesions, while a positive sliding bladder sign makes the presence of severe adhesions unlikely. Establishing vesicouterine adhesions by TVS may optimize preoperative planning, and can be used for future studies to evaluate the relationship between symptomatology and vesicouterine adhesions and, subsequently, the effect of adhesion-prevention interventions. © 2024 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.

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 score0.625
Threshold uncertainty score0.379

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.025
GPT teacher head0.276
Teacher spread0.251 · 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

Citations6
Published2024
Admission routes1
Has abstractyes

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