Overview of guidelines on breast screening: Why recommendations differ and what to do about it
Bibliographic record
Abstract
Updated guidelines on breast cancer screening have been published by several major organisations over the past five years. Recommendations vary regarding both age range, screening interval, and even on whether breast screening should be offered at all. The variation between recommendations reflects substantial differences in estimates of the major benefit (breast cancer mortality reduction) and the major harm (overdiagnosis). Estimates vary considerably among randomised trials, as well as observational studies: from no benefit to large reductions, and from no overdiagnosis to substantial levels. The estimates vary according to the methodology of the randomised trials, and the design of the observational studies. Guideline recommendations reflect the choice of evidence informing them. While there are well-developed tools to deal with randomised trials in guideline work, these are not always used, or they may not be followed as recommended. Further, results of trials performed decades ago may no longer be applicable. For observational studies, the framework for inclusion in guidelines is not similarly well-developed and there are methodological concerns specific to screening interventions, such as small effects in absolute terms. There is a need for agreement on a hierarchy of observational study designs to quantify the major benefit and harm of cancer screening. This review provides a summary of recent guidelines on breast cancer screening and their major strengths and weaknesses, as well as a short overview of the major strengths and limitations of observational study designs. There is a need for agreement on a hierarchy of observational study designs in this field.
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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 | 0.016 | 0.069 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 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".