Detection at All Costs: Is Cancer Screening Always Better?
Bibliographic record
Abstract
Cancer treatment and prevention is a continuously evolving field. Canadian guidelines suggest that women of average breast cancer risk receive mammography screening regularly. Screening seems an intuitive approach to diagnosing cancer in its early and curable stages. However, repeat cancer screening is not without drawbacks; as the intensity and frequency of cancer screening increases, so do the consequences of overdiagnosis, including avoidable testing and treatment, pain, stress, unwillingness to participate in future testing, and healthcare spending. Therefore, achieving optimal screening outcomes requires an accurate understanding of the costs and benefits associated with this procedure. By empowering patients to make informed decisions regarding cancer screening, one potential strategy to improve patient care is to improve communication and decision-making between physicians and patients. Other strategies to achieve better screening include targeted screening programs for high-risk patients that rely on different screening modalities.
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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 | 0.027 | 0.124 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.026 | 0.003 |
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".