Many quality measurements, but few quality measures assessing the quality of breast cancer care in women: A systematic review
Bibliographic record
Abstract
BACKGROUND: Breast cancer in women is increasingly frequent, and care is complex, onerous and expensive, all of which lend urgency to improvements in care. Quality measurement is essential to monitor effectiveness and to guide improvements in healthcare. METHODS: Ten databases, including Medline, were searched electronically to identify measures assessing the quality of breast cancer care in women (diagnosis, treatment, followup, documentation of care). Eligible studies measured adherence to standards of breast cancer care in women diagnosed with, or in treatment for, any histological type of adenocarcinoma of the breast. Reference lists of studies, review articles, web sites, and files of experts were searched manually. Evidence appraisal entailed dual independent assessments of data (e.g., indicators used in quality measurement). The extent of each quality indicator's scientific validation as a measure was assessed. The American Society of Clinical Oncology (ASCO) was asked to contribute quality measures under development. RESULTS: Sixty relevant reports identified 58 studies with 143 indicators assessing adherence to quality breast cancer care. A paucity of validated indicators (n = 12), most of which assessed quality of life, only permitted a qualitative data synthesis. Most quality indicators evaluated processes of care. CONCLUSION: While some studies revealed patterns of under-use of care, all adherence data require confirmation using validated quality measures. ASCO's current development of a set of quality measures relating to breast cancer care may hold the key to conducting definitive studies.
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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".