Should we share breast density information during breast cancer screening in the United Kingdom? an integrative review
Bibliographic record
Abstract
OBJECTIVE: Dense breasts are an established risk factor for breast cancer and also reduce the sensitivity of mammograms. There is increasing public concern around breast density in the UK, with calls for this information to be shared at breast cancer screening. METHODS: We searched the PubMed database, Cochrane Library and grey literature, using broad search terms in October 2022. Two reviewers extracted data and assessed the risk of bias of each included study. The results were narratively synthesised by five research questions: desire for information, communication formats, psychological impact, knowledge impact and behaviour change. RESULTS: We identified 19 studies: three Randomised Controlled Trials (RCTs), three cohort studies, nine cross-sectional studies, one qualitative interview study, one mixed methods study and two 2021 systematic reviews. Nine studies were based in the United States of America (USA), five in Australia, two in the UK and one in Croatia. One systematic review included 14 USA studies, and the other 27 USA studies, 1 Australian and 1 Canadian. The overall GRADE evidence quality rating for each research question was very low to low.Generally, participants wanted to receive breast density information. Conversations with healthcare professionals were more valued and effective than letters. Breast density awareness after notification varied greatly between studies.Breast density information either did not impact frequency of mammography screening or increased the intentions of participants to return for routine screening as well as intention to access, and uptake of, supplementary screening. People from ethnic minority groups or of lower socioeconomic status (SES) had greater confusion following notification, and, along with those without healthcare insurance, were less likely to access supplementary screening. CONCLUSION: Breast density specific research in the UK, including different communities, is needed before the UK considers sharing breast density information at screening. There are also practical considerations around implementation and recording, which need to be addressed. ADVANCES IN KNOWLEDGE: Currently, sharing breast density information at breast cancer screening in the UK may not be beneficial to participants and could widen inequalities. UK specific research is needed, and measurement, communication and future testing implications need to be carefully considered.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".