ArticleJournal of gynecologic oncology2024
Patient and clinician priorities for information on treatment outcomes for advanced ovarian cancer: a Delphi exercise.
Article in Journal of gynecologic oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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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.
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Who cites it
1 citing paper in PubMed.
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Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivePatients with advanced ovarian cancer face a range of treatment options, and there is unwarranted variation in treatment decision-making between UK providers. Decision support tools that produce data on treatment outcomes as a function of individual patient characteristics, would help both patients and clinicians to make informed, preference- and values-based choices. However, data on treatment outcomes to include in such tools are lacking.
methodsFollowing a literature review, a questionnaire was designed for use in a Delphi process to establish which treatment outcomes are important to both patients and clinicians in decision-making for treatment for advanced ovarian cancer. Patient and clinician panels were established.
resultsFollowing 2 Delphi rounds, consensus was achieved for 7/11 items in the patient panel and 8/11 items in the clinician panel. Consensus across both panels was achieved for inclusion of both overall survival and progression free survival as important items in the decision-making process, although there remained differences of opinion as to whether these should be presented as relative or absolute values.
conclusionInformation needs for treatment decision-making in ovarian cancer differ between and within patient and clinician groups. Whilst overall survival and progression free survival are universally accepted as important data items, decision support tools will need to be nuanced to allow presentation of a range of outcomes and associated probabilities, and in a range of formats, that can be tailored to the preferences of clinician and patients.
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