ArticleBMJ open2025
Preferred labels and language to improve communication about lesions at low risk of progressing to cancer: qualitative interviews with patients and physicians.
Article in BMJ open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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
2 citing papers in PubMed.
- Labels, Language and Other Strategies to Improve Communication About Lowest Grade Ductal Carcinoma in Situ: Qualitative Interviews With Women and Physicians.Psycho-oncology · 2026Article
- Labels, Language, and Other Strategies to Improve Communication About Lower Grade Forms of Ductal Carcinoma In Situ of the Breast: A National Delphi Survey.International journal of breast cancer · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesWe explored how to improve communication about low-risk lesions including labels, language and other strategies.
designQualitative description and thematic analysis to examine the transcripts of telephone interviews with patients who had low-risk lesions and physicians; and mapping to Communication Accommodation Theory to interpret themes.
settingCanada
participants15 patients: 6 (40%) bladder, 5 (33%) prostate and 4 (27%) cervix lesions; and 13 physicians: 7 (54%) cervix, 3 (23%) bladder and 3 (23%) prostate lesions.
main outcome measuresPatient and physician views of labels, language and other strategies to improve communication about low-risk lesions.
resultsPatients and clinicians held discordant views about low-risk lesion label impact, preferences and rationale. All labels prompted confusion and anxiety among patients. In contrast, physicians perceived that patients understood that labels they used across all label categories (abnormal, precursor-to-cancer and cancer) implied low risk for cancer progression. Patients preferred abnormal cells, particularly when first learning of their diagnosis, and desired additional information to distinguish their diagnosis from cancer and justify treatment. In contrast, physicians favoured precursor-to-cancer and cancer labels out of habit, to match labels that patients saw elsewhere (online, charts) and to convince patients to attend follow-up and treatment visits. However, patients and physicians largely agreed on the need for 16 strategies that could improve communication about low-risk lesions including language (eg, plain language, situate low-risk lesions on cancer spectrum) and complementary communication strategies (eg, longer appointments, visual aids, connect patients with support services or groups).
conclusionsThe findings build on prior research by revealing that modifying labels is not the only or best strategy needed to improve communication about low-risk lesions. Ongoing research should examine how best to implement the strategies recommended by patients and physicians.
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.