Chemoprevention: A new concept for cancer prevention in primary care
Bibliographic record
Abstract
Background: Prevention of cancer in primary care has focused on modifying behaviours associated with increased risk of cancer (primary prevention) or increasing participation in national cancer screening programs (secondary prevention). On the basis of metaanalyses of large prevention trials, a new paradigm in primary prevention – chemoprevention – is beginning to enter the realms of primary care for specific populations. Objectives: In this article, we discuss two examples of cancer chemoprevention relevant to general practice: low-dose aspirin for the prevention of colorectal cancer in people aged 50–70 years, and selective oestrogen receptor modulators (SERMs) for women at increased risk of breast cancer. We present new expected frequency trees that show the absolute benefits and harms of taking these medications in specific populations. Discussion: These expected frequency trees can serve as risk-communication aids to support shared decision making and the implementation of new chemoprevention guidelines in general practice.
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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 | 0.025 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.003 | 0.026 |
| Scholarly communication | 0.007 | 0.018 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".