Evidence map›Paper›PMID 42104671›Full record

ArticleThe Australian & New Zealand journal of obstetrics & gynaecology2026

The Incidence of Negative Laparoscopy for Pelvic Pain Stratified by Level of Training and Location of Service Provision.

Akshara Shyamsunder, Cecilia Ng, Zaynab El-Hamawi, David Krones, Erin Nesbitt-Hawes, Rebecca Deans, Jason Abbott

Abstract read
In one paragraph

Article in The Australian & New Zealand journal of obstetrics & gynaecology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Akshara ShyamsunderDiscipline of Women's Health, School of Clinical Medicine, UNSW Sydney, Sydney, New South Wales, Australia.ORCID https://orcid.org/0009-0009-5738-3501
Cecilia NgDiscipline of Women's Health, School of Clinical Medicine, UNSW Sydney, Sydney, New South Wales, Australia.
Zaynab El-HamawiDiscipline of Women's Health, School of Clinical Medicine, UNSW Sydney, Sydney, New South Wales, Australia.
David KronesDiscipline of Women's Health, School of Clinical Medicine, UNSW Sydney, Sydney, New South Wales, Australia.
Erin Nesbitt-HawesDiscipline of Women's Health, School of Clinical Medicine, UNSW Sydney, Sydney, New South Wales, Australia.
Rebecca DeansDiscipline of Women's Health, School of Clinical Medicine, UNSW Sydney, Sydney, New South Wales, Australia.
Jason AbbottDiscipline of Women's Health, School of Clinical Medicine, UNSW Sydney, Sydney, New South Wales, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

aimSystematic reduction of negative laparoscopy for pelvic pain is crucial to reducing surgical morbidity, improving diagnostic accuracy and minimising cost. This study aims to determine the incidence and consider the underlying causes of negative laparoscopy in women presenting with pelvic pain.

methodsA 5-year, retrospective cohort study was undertaken for women undergoing laparoscopy for pelvic pain. Patient selection was from an Australian tertiary public hospital with both non-fellowship trained gynaecologists and an Australasian Gynaecological Endoscopic Surgery (AGES) accredited training programme (fellowship trained) as well as one private clinic comprising two fellowship trained gynaecologists. Data was collected from the medical records. A 'negative' laparoscopy was defined either visually or by vision and negative biopsy on histology. The rate of negative laparoscopy in the public and private sector was compared using an odds ratio.

resultsOf 1309 women, 174 (13%) had a negative laparoscopy. The negative laparoscopy rate was significantly higher amongst non-fellowship trained gynaecologists compared to those with fellowship training (OR = 2.48; 95% CI: 1.76-3.43, p < 0.05). Visually negative laparoscopy was made in 48/174 (28%) of cases, all from the public sector, with 41/48 (85%) of laparoscopies without biopsy performed by non-fellowship trained gynaecologists. A 56% discordance between intraoperative visualisation and histopathological findings was identified.

conclusionOverall, negative laparoscopy rates are low compared to previously reported data. The discrepancy between the healthcare sectors likely relates to patient factors, surgical training, and clinician bias. Peritoneal biopsy for symptomatic patients undergoing laparoscopy should be considered due to the potential to miss superficial disease.

Indexed as

GynecologyLaparoscopyPelvic PainAdultAustraliaEndometriosisFellowships and ScholarshipsFemaleGynecologistsHospitals, PublicHumansIncidenceMiddle AgedPrivate SectorRetrospective Studiesendometriosisfellowship‐trainedlaparoscopypelvic painprivatepublic

Identifiers

PMID42104671
PMCPMC13156528

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.