Jodie C Avery

Gynecology — University of Adelaide, Australia

LeadR Profile

Jodie C Avery University of Adelaide Australia
211 ScienceLeadR Reputation 211
Network Map

Gynecology

Subspecialties: Medical Imaging - Obstetrics - Public Health

Full career – Last 3 years

Main Topics

Publications and Clinical Studies

Publications – Clinical Studies

Profile data updated on

Organizations

Career arc: 1999 - 2026

Source: PubMed

University of Adelaide

could not re-read experiences after save", error); return null; } if (persistedEntries === null) { return null; } if (persistedEntries.length === 0 && sent.some((entry) => entry.isvisible !== false)) { console.warn(" re-read after save came back empty, keeping the edited entries"); return null; } this.state.current.experience = persistedEntries; const expected = this.yearsSignature(sent); const received = this.yearsSignature(persistedEntries.map((entry) => ({ isvisible: entry.isvisible, topresent: entry.topresent, year_start: entry.startperiod, year_stop: entry.endperiod }))); if (expected !== received) { console.error(" saved experiences were not persisted as sent", { sent: expected, received: received }); return false; } return true; }, async fetchExperiences() { const response = await $.ajax({ url: ajaxLoadUserData, type: "POST", data: { security: ajaxDataLoadUserData.security, data_to_load: "OrganizationHisto", } }); const parsedData = JSON.parse(response); if (parsedData.loaded === false || !parsedData.user_data || !Array.isArray(parsedData.user_data)) { return null; } return parsedData.user_data.map((value, index) => ({ company: value["name"], url: "", position: (value.hasOwnProperty("position") ? ( value["position"] ) : ( "" ) ), startperiod: value["year_start"], endperiod: value["year_stop"], topresent: (value.hasOwnProperty("topresent") ? ( value["topresent"] ) : ( false ) ), orgid: value["orgid"], isvisible: (value.hasOwnProperty("isvisible") ? ( value["isvisible"] ) : ( true ) ), description: "", tags: [], isOpen: false, ismain: (value.hasOwnProperty("ismain") ? ( value["ismain"] ) : ( (index === 0) ? true : false ) ) })); }, async loadDataOnMount() { this.loading = true; try { const experiences = await this.fetchExperiences(); this.loadFailed = experiences === null; if (experiences !== null) { this.state.current.experience = experiences; } } catch (error) { console.warn(" could not load experiences", error); this.loadFailed = true; } this.loading = false; } }, computed: { experience: { get() { return this.state.current.experience }, }, }, async mounted() { await this.loadDataOnMount() }, } }; })

Publications Synthesis

Sources: PubMed, OpenAlex

Number : 77

Citations

Citations: 1 261 1st author: 19 Last author: 4 Unique author: 2


Best Journals

Breakdown by type

Click a legend label to hide or show it in the chart.

Latest Publications

5 most recent indexed articles

Source: Pubmed

Title Journal Year Citations
The problem with the 'truth': rethinking ground truth for artificial intelligence in endometriosis diagnosis.
endometriosis, intelligence, reproduction, machine learning, embryology, diagnosis, artificial intelligence
Hum Reprod20260
Sexual and reproductive health policies for migrant, immigrant and refugee populations in select high-income countries: a policy analysis protocol.
income, reproductive health
BMJ Open20250
Burdens and risk factors for mortality in adolescents and young adults aged 10-24 years in Australia and comparison with OECD countries between 2000 and 2021: Global Burden of Disease Study 2021.
mortality, cause of death, global burden of disease, aged, adolescent, risk factors, morbidity, cost of illness
BMJ Public Health20250
Global trends in the uptake of specialist diagnostic ultrasound and MRI scans for endometriosis: An international cross-sectional survey.
genital diseases, female, endometriosis, cross-sectional studies, ultrasonography, obstetrics, magnetic resonance imaging, laparoscopy, radiography, gynecology, diagnosis, research, gynecologists
Int J Gynaecol Obstet20250
Development of a Novel Machine Learning Model for Automatic Assessment of Quality of Transvaginal Ultrasound Images From Multi-Annotator Labels.
pathology, multiple chronic conditions, machine learning, diagnosis, learning
Australas J Ultrasound Med20251

LeadR Networks

Main global connection map