ArticleThe patient2023
Patient Preferences in Diagnostic Imaging: A Scoping Review.
Article in The patient, 2023. 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
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.
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.
Who cites it
1 citing paper in PubMed, 3 citations in OpenAlex.
- Patient, Physician, and Caregiver Preferences for Lung Cancer Treatment: A Systematic Review of Discrete Choice Experiments.Healthcare (Basel, Switzerland) · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 3 institutions in 1 country.
Funding
Abstract
backgroundAs new diagnostic imaging technologies are adopted, decisions surrounding diagnostic imaging become increasingly complex. As such, understanding patient preferences in imaging decision making is imperative.
objectivesWe aimed to review quantitative patient preference studies in imaging-related decision making, including characteristics of the literature and the quality of the evidence.
methodsThe Pubmed, Embase, EconLit, and CINAHL databases were searched to identify studies involving diagnostic imaging and quantitative patient preference measures from January 2000 to June 2022. Study characteristics that were extracted included the preference elicitation method, disease focus, and sample size. We employed the PREFS (Purpose, Respondents, Explanation, Findings, Significance) checklist as our quality assessment tool.
resultsA total of 54 articles were included. The following methods were used to elicit preferences: conjoint analysis/discrete choice experiment methods (n = 27), contingent valuation (n = 16), time trade-off (n = 4), best-worst scaling (n = 3), multicriteria decision analysis (n = 3), and a standard gamble approach (n = 1). Half of the studies were published after 2016 (52%, 28/54). The most common scenario (n = 39) for eliciting patient preferences was cancer screening. Computed tomography, the most frequently studied imaging modality, was included in 20 studies, and sample sizes ranged from 30 to 3469 participants (mean 552). The mean PREFS score was 3.5 (standard deviation 0.8) for the included studies.
conclusionsThis review highlights that a variety of quantitative preference methods are being used, as diagnostic imaging technologies continue to evolve. While the number of preference studies in diagnostic imaging has increased with time, most examine preventative care/screening, leaving a gap in knowledge regarding imaging for disease characterization and management.
Indexed as
Identifiers
What Socratic holds
Registered trials
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 Socratic graph.