Balancing Shared Decision-Making with Population-Based Recommendations: A Policy Perspective of PSA Testing and Mammography Screening
Bibliographic record
Abstract
Abstract Population-based screening programs invite otherwise healthy people who are not experiencing any symptoms to be screened for cancer. In the case of breast cancer, mammography screening programs are not intended for higher risk groups, such as women with family history of breast cancer or carriers of specific gene mutations, as these women would receive diagnostic mammograms. In the case of prostate cancer, there are no population-based screening programs available, but considerable access and use of opportunistic testing. Opportunistic testing refers to physicians routinely ordering a PSA test or men requesting it at time of annual appointments. Conversations between patients and their physicians about the benefits and harms of screening/testing are strongly encouraged to support shared decision-making. There are several issues that make this risk scenario contentious: cancer carries a cultural dimension as a ‘dread disease’; population-based screening programs focus on recommendations based on aggregated evidence, which may not align with individual physician and patient values and preferences; mantras that ‘early detection is your best protection’ make public acceptance of shifting guidelines based on periodic reviews of scientific evidence challenging; and while shared decision-making between physicians and patients is strongly encouraged, meaningfully achieving this in practice is difficult. Cross-cutting these tensions is a fundamental question about what role the public ought to play in cancer screening policy.
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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 | 0.031 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.024 |
| Scholarly communication | 0.019 | 0.013 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.016 | 0.014 |
| Insufficient payload (model declined to judge) | 0.011 | 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".