Effects of awareness of breast cancer overdiagnosis among women with screen-detected or incidentally found breast cancer: a qualitative interview study
Bibliographic record
Abstract
OBJECTIVES: To explore experiences of women who identified themselves as having a possible breast cancer overdiagnosis. DESIGN: Qualitative interview study using key components of a grounded theory analysis. SETTING: International interviews with women diagnosed with breast cancer and aware of the concept of overdiagnosis. PARTICIPANTS: Twelve women aged 48-77 years from the UK (6), USA (4), Canada (1) and Australia (1) who had breast cancer (ductal carcinoma in situ n=9, (invasive) breast cancer n=3) diagnosed between 2004 and 2019, and who were aware of the possibility of overdiagnosis. Participants were recruited via online blogs and professional clinical networks. RESULTS: Most women (10/12) became aware of overdiagnosis after their own diagnosis. All were concerned about the possibility of overdiagnosis or overtreatment or both. Finding out about overdiagnosis/overtreatment had negative psychosocial impacts on women's sense of self, quality of interactions with medical professionals, and for some, had triggered deep remorse about past decisions and actions. Many were uncomfortable with being treated as a cancer patient when they did not feel 'diseased'. For most, the recommended treatments seemed excessive compared with the diagnosis given. Most found that their initial clinical teams were not forthcoming about the possibility of overdiagnosis and overtreatment, and many found it difficult to deal with their set management protocols. CONCLUSION: The experiences of this small and unusual group of women provide rare insight into the profound negative impact of finding out about overdiagnosis after breast cancer diagnosis. Previous studies have found that women valued information about overdiagnosis before screening and this knowledge did not reduce subsequent screening uptake. Policymakers and clinicians should recognise the diversity of women's perspectives and ensure that women are adequately informed of the possibility of overdiagnosis before screening.
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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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".