RE: Informing women about overdetection in breast cancer screening: Two-year outcomes from a randomized trial
Bibliographic record
Abstract
The trial by Hersch et al. (1) entitled “Informing Women About Overdetection in Breast Cancer Screening: Two-Year Outcomes From a Randomized Trial” found that informing women of overdiagnosis with a decision aid improved knowledge of overdiagnosis but did not affect participation rates. Several other recent studies have similar findings, and these studies also only used decision aids (2-4). As a family physician, I have had strikingly different results with informing women. Since I started informing women in 2019, breast cancer screening rates for eligible low-risk women in my practice have decreased from 55% to 30%. I use the Canadian Task force 1000 women-chart (5) and spend no more than 5 minutes explaining the risk of false positives as well as overdiagnosis compared with the mortality benefit. Women are surprised and their typical response was “Why would I do the screening, with results like this?” My results may differ from published results because I directly discuss screening with women at the time of their decision. This corresponds with a Cochrane systematic review that found high-quality evidence decision aids can increase knowledge and better inform patients, but low-quality evidence helps people make decisions that are consistent with their values (6). Compounding this is the fact that overdiagnosis is difficult to communicate and an inherently difficult concept to understand (7). The intention to provide women a decision aid to make a more informed choice on breast cancer screening is admirable. However, to truly inform women and determine whether well-informed women choose less screening, a randomized controlled trial with women being informed via a direct conversation—ideally with a trusted health-care professional—at the time they make their decision would be advantageous. In addition, overdiagnosis may not only affect the women but may also have a multiplicative effect on their family members. Twenty-five percent of women 50-74 years old in my practice had a first-degree relative with breast cancer. Based on current evidence of overdiagnosis, upwards of one-quarter of these women may have been overdiagnosed (5), which means a significant proportion of family members of women diagnosed may unnecessarily carry the label and the corresponding anxiety of “higher risk for breast cancer” for their entire life. No funding was used for this correspondence. Role of the funder: Not applicable. Disclosure: The author declares no competing interests. Author contributions: Conceptualization, writing—original draft, writing—review & editing: RK. Acknowledgements: The author thanks Michelle Umali, office administrator at the medical clinic, for data analysis support, and Dr James Dickinson for review of the draft correspondence. There were no new data generated or used in this correspondence.
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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 | 0.019 | 0.099 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.018 | 0.011 |
| Insufficient payload (model declined to judge) | 0.024 | 0.005 |
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".