Overview of guidelines on breast screening: Why recommendations differ and what to do about it
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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) | 0.001 | 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 it