64 <i>‘First, do no harm’</i>Overdiagnosis of breast cancer at mammography screening: understanding the influence of methods of communicating overdiagnosis risk on informed decision-making
Bibliographic record
Abstract
Overdiagnosis is an inadequately addressed harm of breast cancer (BC) screening. Overdiagnosis is the diagnosis of BC that would not have been diagnosed in the patient’s lifetime without screening. Knowledge of this risk of mammography screening in patients is low. Previously, patient information leaflets have recommended BC screening without acknowledging the risk of overdiagnosis. Evidence-based, objective risk communication supports patients’ autonomy to make informed choices (IC) about attending mammography. The most influential method of communicating overdiagnosis risk at BC screening on informed decision-making (IDM) has not been determined. Risk communication encompasses the four principles of medical ethics: autonomy, beneficence, justice and non-maleficence. Aim To assess the influence of methods of overdiagnosis risk communication on IDM for mammography screening. This review focuses on the outcome of IDM rather than screening participation. Objectives Describe the current literature, analyse the quality of the evidence, and provide recommendations. Method A scoping review format was used to represent the breadth of literature available via differing study designs. The PRISMA-ScR checklist from the EQUATOR Network was followed. Inclusion criteria included studies published between 2000 and February 2023 in English. Participants include all patients eligible for BC screening via mammography without a personal history of BC. To be included, studies must have investigated risk communication before screening to assist an actual mammography screening decision. Risk communication should include all risks of mammography, including overdiagnosis, to be eligible. In controlled studies, the detail or format of risk communication must differ, including overdiagnosis risk communication. Finally, included studies must measure the outcome of IC. MEDLINE, EMBASE and CINAHL databases were searched in February 2023. References lists of identified studies were manually screened. The quality of studies was assessed using Joanna Briggs Institute (JBI) critical appraisal tools and Cochrane’s risk of bias tools. Results 159 studies were screened. 11 studies were included in this review, including seven RCTs, two quasi-experimental studies and two systematic reviews. Of nine primary studies, seven assessed the influence of decision aids (DAs), one provided a presentation and one a standard brochure. Only two assessed overdiagnosis risk communication solely. Six of seven RCTs reported a significant increase in the proportion of participants with IC, with a postintervention mean of 50% and ranged from 24% to 73.5%. In all RCTs, the proportion of participants with ‘adequate’ knowledge significantly increased, with a postintervention mean of 54% and ranged from 23.1% to 95%. Screening participation showed no significant difference between groups. The risk communication resources and outcome assessment methods varied considerably, so direct comparisons between studies could not be drawn. All seven RCTs had a high risk of bias overall and in the outcome assessment domain. Conclusions The findings suggest that risk communication positively influences IDM in mammography screening decisions. However, there is minimal high-quality evidence investigating the communication of overdiagnosis risk. A standardised method for assessing mammography screening DAs should be validated, including a standardised control resource and outcome assessment methods. Standardised methods allow for comparison between studies to ascertain the most influential method of risk communication. Future research is required to increase IDM further and solely investigate overdiagnosis risk communication. Risk communication, including overdiagnosis risk, should be provided to patients invited to mammography screening and their effectiveness studied. Future risk communication resources should be individualised by including personalised risk calculators. Risk communication should follow the potential shift towards personalised, risk-based BC screening. Finally, screening programmes should shift their focus from increasing participation to increasing IDM, a more ethical measure of a screening programme’s performance. Future approaches should endeavour to ‘first, do no harm’
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.068 | 0.182 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".