Comparison of International Breast Cancer Guidelines: Are We Globally Consistent? Cancer Guideline AGREEment
Bibliographic record
Abstract
BACKGROUND: Evidence-based guidelines are used in health care systems throughout the world to aid in treatment decisions and to ensure quality and consistency in patient care. In breast oncology, guidelines for care are published by several internationally recognized organizations, including those from the United States, Canada, and the United Kingdom. The present study compared clinical breast cancer guidelines from the American Society of Clinical Oncology (ASCO, United States), Cancer Care Ontario (CCO, Canada), and the National Institute for Health and Clinical Excellence (NICE, United Kingdom) to determine the quality and consistency of content across international organizations. METHODS: We searched for breast cancer guidelines published by ASCO, CCO, and NICE. Guidelines on the same theme were identified across organizations and appraised by 4 independent reviewers using the Appraisal of Guidelines for Research and Evaluation (AGREE) instrument. Content of each guideline was also scored for consistency in overall recommendations across organizations and for consistency in cited evidence. RESULTS: The quality of breast cancer guidelines produced by the targeted organizations was consistently good in the areas of Scope and Purpose, Rigor of Development, and Clarity and Presentation, but variable in the domains of Stakeholder Involvement, Applicability, and Editorial Independence. The content of the guidelines varied slightly in the strength of their recommendations. CONCLUSIONS: Our review demonstrated consistency in quality and content for breast cancer practice guidelines published by various organizations. Future guidelines developed by these organizations should focus on how to implement and measure uptake of a guideline.
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.
How this classification was reachedexpand
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 | 0.001 |
| 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) | 0.005 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".