A systematic review and quality appraisal of international guidelines for early breast cancer systemic therapy: Are recommendations sensitive to different global resources?
Bibliographic record
Abstract
The breast cancer incidence in low and middle income countries (LMCs) is increasing globally, and patient outcomes are generally worse in these nations compared to high income countries (HICs). This is partly due to resource constraints associated with implementing recommended breast cancer therapies. Clinical practice guideline (CPG) adherence can improve breast cancer outcomes, however, many CPGs are created in HICs, and include costly recommendations that may not be feasible in LMCs. In addition, the quality of CPGs can be variable. The aim of this study was to perform a systematic review of CPGs on early breast cancer systemic therapy with potential international impact, to evaluate their content, quality, and resource sensitivity. A MEDLINE and gray literature search was completed for English language CPGs published between 2005 and 2010, and then updated to July 2014. Extracted guidelines were evaluated using the AGREE 2 instrument. Guidelines were specifically analyzed for resource sensitivity. Most of the extracted CPGs had similar recommendations with regards to systemic therapy. However, only one, the Breast Health Global Initiative, made recommendations with consideration of different global resources. Overall, the CPGs were of variable quality, and most scored poorly in the quality domain evaluating implementation barriers such as resources. Published CPGs for early breast cancer are created in HICs, have similar recommendations, and are generally resource-insensitive. Given the visibility and influence of these CPGs on LMCs, efforts to create higher quality, resource-sensitive guidelines with less redundancy are needed.
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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 | 0.160 | 0.542 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.014 | 0.013 |
| Bibliometrics | 0.029 | 0.027 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| 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".