Evidence map›Paper›PMID 37076147›Full record

ArticleBMJ open2023

Gaining consensus on clinical quality outcomes for eating disorders: Framework for the development of an Australian national minimum dataset.

Emma Bryant, Catherine Broomfield, Jennifer Burrows, Sian McLean, Peta Marks, Danielle Maloney, Stephen Touyz, Sarah Maguire

Abstract read
In one paragraph

Article in BMJ open, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

8 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Emma BryantInsideOut Institute for Eating Disorders, Faculty of Medicine and Health, University of Sydney and Sydney Local Health District, Camperdown, Sydney, Australia emma.bryant@sydney.edu.au.ORCID 0000-0003-1918-6020
Catherine BroomfieldInsideOut Institute for Eating Disorders, Faculty of Medicine and Health, University of Sydney and Sydney Local Health District, Camperdown, Sydney, Australia.
Jennifer BurrowsInsideOut Institute for Eating Disorders, Faculty of Medicine and Health, University of Sydney and Sydney Local Health District, Camperdown, Sydney, Australia.
Sian McLeanDepartment of Psychology, Counselling and Therapy, La Trobe University, Melbourne, Victoria, Australia.
Peta MarksInsideOut Institute for Eating Disorders, Faculty of Medicine and Health, University of Sydney and Sydney Local Health District, Camperdown, Sydney, Australia.
Danielle MaloneyInsideOut Institute for Eating Disorders, Faculty of Medicine and Health, University of Sydney and Sydney Local Health District, Camperdown, Sydney, Australia.
Stephen TouyzInsideOut Institute for Eating Disorders, Faculty of Medicine and Health, University of Sydney and Sydney Local Health District, Camperdown, Sydney, Australia.
Sarah MaguireInsideOut Institute for Eating Disorders, Faculty of Medicine and Health, University of Sydney and Sydney Local Health District, Camperdown, Sydney, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesEating disorders (EDs) are complex psychiatric illnesses requiring multidisciplinary care across both mental and medical healthcare settings. Currently, no nationally comprehensive, consistent, agreed on or mandated data set or data collection strategy exists for EDs in Australia: thus, little is known about the outcomes of care nor treatment pathways taken by individuals with EDs. InsideOut Institute was contracted by the Australian Government Department of Health to develop a minimum dataset (MDS) for the illness group with consideration given to data capture mechanisms and the scoping of a national registry.

designA four-step modified Delphi methodology was used, including national consultations followed by three rounds of quantitative feedback by an expert panel.

settingDue to social distancing protocols throughout the global SARS-CoV-2 pandemic, the study was conducted online using video conferencing (Zoom and Microsoft Teams) (Step 1), email communication and the REDCap secure web-based survey platform (Steps 2-4).

participants14 data management organisations, 5 state and territory government departments of health, 2 Aboriginal and Torres Strait Islander advising organisations and 28 stakeholders representing public and private health sectors across Australia participated in consultations. 123 ED experts (including lived experience) participated in the first quantitative round of the Delphi survey. Retention was high, with 80% of experts continuing to the second round and 73% to the third.

main outcome measuresItems and categories endorsed by the expert panel (defined a priori as >85% rating an item or category 'very important' or 'imperative').

resultsHigh consensus across dataset items and categories led to the stratification of an identified MDS. Medical status and quality of life were rated the most important outcomes to collect in an MDS. Other items meeting high levels of consensus included anxiety disorders, depression and suicidality; type of treatment being received; body mass index and recent weight change.

conclusionsUnderstanding presentation to and outcomes from ED treatment is vital to drive improvements in healthcare delivery. A nationally agreed MDS has been defined to facilitate this understanding and support improvements.

Indexed as

COVID-19Feeding and Eating DisordersAustraliaAustralian Aboriginal and Torres Strait Islander PeoplesConsensus Statements as TopicHumansQuality of LifeSARS-CoV-2eating disordershealth policyhealth services administration & managementpatient reported outcome measuresquality in health careregistries

Identifiers

PMID37076147
PMCPMC10124290

What OpenQuestion holds

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LicenceCC BY-NC
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Registered trials

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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.