Evidence map›Paper›PMID 30091121›Full record

Trial reportJournal of general internal medicine2019

Randomized Trial of Personalized Breast Density and Breast Cancer Risk Notification.

Jennifer S Haas, Catherine S Giess, Kimberly A Harris, Julia Ansolabehere, Celia P Kaplan

Registry-linked trialAbstract readRandomized Controlled Trial
In one paragraph

Trial report in Journal of general internal medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT02986360 (Informed Implementation of Breast Density Reporting), which is not on this map. Cited by 12 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed, 3 pooled it
–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.

NCT02986360 nacompletednot on this map

Informed Implementation of Breast Density Reporting

TypeinterventionalSponsorBrigham and Women's HospitalRan2017 to 2017Enrolled500ConditionsBreast Density, Breast Cancer PreventionArmsVidscrip educational video
3 · Its place in the literature

Who cites it

12 citing papers in PubMed, 3 syntheses or guidelines pooled it.

  1. Personalizing cancer risk: a systematic review of risk communication strategies.Journal of cancer research and clinical oncology · 2026
    Pooled it
  2. Pooled it
  3. Pooled it
  4. Trial
  5. Trial
  6. Article
  7. Reporting breast density on chest computed tomography.Translational breast cancer research : a journal focusing on translational research in breast cancer · 2023
    Review
  8. Article
  9. Article
  10. Article
  11. Breast Density Legislation and the Promise Not Attained.Journal of general internal medicine · 2019
    Article
  12. Article
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

5 authors.

Jennifer S HaasDivision of General Medicine and Primary Care, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA. jhaas@bwh.harvard.edu.
Catherine S GiessDepartment of Radiology, Division of Breast Imaging, Brigham and Women's Hospital, Boston, MA, USA.
Kimberly A HarrisDivision of General Medicine and Primary Care, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA.
Julia AnsolabehereDivision of General Medicine and Primary Care, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA.
Celia P KaplanDivision of General Internal Medicine, Department of Medicine, University of California, San Francisco, San Francisco, CA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDespite widespread implementation of mammographic breast density (MBD) notification laws, the impact of these laws on knowledge of MBD and knowledge of breast cancer risk is limited by the lack of tools to promote informed decision-making in practice.

objectiveTo develop and evaluate whether brief, personalized informational videos following a normal mammogram in addition to a legislatively required letter about MBD result can improve knowledge of MBD and breast cancer risk compared to standard care (i.e., legislatively required letter about MBD included with the mammogram result). DESIGN/

participantsProspective randomized controlled trial of English-speaking women, age 40-74 years, without prior history of breast cancer, receiving a screening mammogram with a normal or benign finding (intervention group n = 235, control group n = 224).

interventionbrief (3-5 min) video, personalized to a woman's MBD result and breast cancer risk. MAIN MEASURES: Primary outcomes were a woman's knowledge of her MBD and risk of breast cancer. Secondary outcomes included whether a woman reported that she discussed the results of her mammogram with her primary care provider (PCP). KEY

resultsRelative to women in the control arm, women in the intervention arm had greater improvement in their knowledge of both their personal MBD (intervention pre/post 39.2%/ 77.5%; control pre/post 36.2%/ 37.5%; odds ratio (OR) 5.34 for change for intervention vs. control, 95% confidence interval (CI) 3.87-7.36; p < 0.001) and risk of breast cancer (intervention pre/post: 66.8%/74.0%; control pre/post 67.9%/ 65.2%; OR 1.42, 95% confidence interval (CI) 1.09-1.84; p = 0.01). Women in the intervention group were more likely than those in the control group to report discussing the results of their mammogram with their PCP (p = 0.05).

conclusionsBrief, personalized videos following mammography can improve knowledge of MBD and personal risk of breast cancer compared to a legislatively mandated informational letter. Trial Registration Clinicaltrials.gov (NCT02986360).

Indexed as

Breast DensityHealth Knowledge, Attitudes, PracticeAdultAgedBreast NeoplasmsEarly Detection of CancerFemaleHumansMammographyMass ScreeningMiddle AgedRisk AssessmentSurveys and QuestionnairesVideo Recordingbreast cancer riskbreast densitymammographypatient education

Identifiers

PMID30091121
PMCPMC6445917

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.