Health professionals’ perspectives on breast cancer risk stratification: understanding evaluation of risk versus screening for disease
Bibliographic record
Abstract
BACKGROUND: Younger women at higher-than-population-average risk for breast cancer may benefit from starting screening earlier than presently recommended by the guidelines. The Personalized Risk Stratification for Prevention and Early Detection of Breast Cancer (PERSPECTIVE) approach aims to improve the prevention of breast cancer through differential screening recommendations based on a personal risk estimate. In our study, we used deliberative stakeholder consultations to engage health professionals in an in-depth dialog to explore the feasibility of the proposed implementation strategies for this new personalized breast cancer screening approach. METHODS: Deliberative stakeholder consultation is a qualitative descriptive study design used to engage health professionals in the discussion, while the mediators play a more passive role. A purposeful sample of 11 health professionals (family physicians and genetic counselors) working in Montreal was used. The deliberations were organized in two phases, including small group deliberations according to the deliberants' health profession and a mixed group deliberation combining participants from the small groups. Inductive thematic content analysis was performed on the transcripts by two coders to create the deliberative and analytic outputs. Quality of deliberations was assessed quantitatively using the de Vries method and qualitatively using participant observation. RESULTS: One of our key findings was that health professionals lacked understanding of the two steps of the screening approach: risk stratification "screening," which is an evaluation for the level of risk and screening for disease. As part of this confusion, the main topic of concern was a justification of program implementation as a population-wide screening, based on their uncertainty that it will be beneficial for women with near-population risks. Despite the noted difficulties concerning implementation, health professionals acknowledged the substantial benefits of the proposed PERSPECTIVE program. CONCLUSIONS: Our study was the first to evaluate the perspectives of health professionals on the implementation and benefits of a new program for breast cancer risk stratification with the purpose of personalizing screening for disease. This new multi-step approach to screening requires more clarity in communication with health professionals. To implement and maintain effective screening, engagement of family physicians with other health professionals or even development of a centralized public health system may be needed.
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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 | 0.009 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".