Development of Quality Indicators in the Management of Breast Cancer: A Systematic Review
Bibliographic record
Abstract
Abstract Background Breast cancer is one of the leading causes of cancer-related death. Current evidence suggests a gap between what is understood to be standard breast cancer management and what happens in clinical practice. The development and implementation of breast quality indicators (QIs) for breast cancer management is one way to achieve better care. This systematic review aimed to identify QIs developed for the management of breast cancer and to summarize characteristics and range of measures uncovered. Methods Studies related to the development of QIs for management and monitoring of breast cancer care were systematically searched, extracted and reviewed using four electronic databases (MEDLINE, EMBASE, CINAHL and Cochrane Library) following a Prospero Protocol Registration (CRD42020207945). The study was reported using the Preferred Reporting Items for the Systematic Review and Meta-analysis (PRISMA). This review reported on the development of QIs in the management of breast cancer and Donabedian's framework was adopted as the analytical framework. Results Out of 1161 potentially relevant articles identified, eight studies met the inclusion criteria and were directly concerned with QI development for breast cancer care. These included two papers from China and one each from; The Netherlands, Belgium, Scotland and Canada. The remaining two were a collaboration among the European Society of Breast cancer Specialists (EUSOMA). The methods used by these studies to identify and develop QIs included a comprehensive literature review, medical records review, clinical guidelines, and Delphi consensus using an expert panel discussion. A total of 38 QIs were identified and classified as: structure (n = 3); process (n = 30); and outcome (n = 5). Structure indicators included: the availability of Multi-Disciplinary Team Meeting (MDT), medical records and breast cancer research infrastructure. Process indicators included eight diagnostic QIs, 22 treatment QIs and seven follow-up QIs. The outcome indicator focused mainly on the overall five-year survival statistics. Conclusions The development of QIs appears relevant to monitor clinical management and performance but is currently limited to higher income countries. Development and implementation of QIs in LMICs countries will improve practice.
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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 | 0.055 | 0.164 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.013 |
| Bibliometrics | 0.016 | 0.020 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.003 |
| 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".