Breast Cancer Guidelines in Canada: A Review of Development and Implementation
Bibliographic record
Abstract
A series of specific clinical practice guidelines (CPGs) were published in Canada in 1998. A primary objective of these 'Clinical Practice Guidelines for the Care and Treatment of Breast Cancer' was to decrease the variation in breast cancer care across the country. Prior to this, researchers found moderate compliance with consensus recommendations for breast cancer therapies in several Canadian provinces. However, a recent study concluded that the publication of the Canadian CPGs did not reduce variations in surgical care for breast cancer. If guidelines are to achieve their intended objectives, they must be implemented in ways that support, encourage, and facilitate their use. Evidence strongly suggests the simple publication and passive dissemination of CPGs are usually ineffective in changing how physicians actually care for patients. CPG implementation, therefore, requires active knowledge translation processes to ensure that the evidence is relevant to all with a stake in bettering breast cancer care. For example, implementation strategies that use computerized CPGs can make evidence-based decision-making routine practice in the clinical setting. The breast cancer community can also work with the newly formed Canadian Partnership Against Cancer to find ways to more successfully support and facilitate guideline use considering the local context.
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 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 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.013 | 0.024 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".