Breast Cancer Screening Should Embrace Precision Medicine: Evidence by Reviewing Economic Evaluations in China
Bibliographic record
Abstract
The cost-effectiveness of conventional population-based breast cancer screening strategies (e.g. mammography) has been found controversial, while evidence shows that genetic testing for early detection of pathogenic variants is cost-effective. We aimed to review the economic evaluations of breast cancer screening in China to provide an information summary for future research on this topic. We searched the literature to identify the economic evaluations that examined breast cancer screening and testing in China, supplemented by hand-searching the reference lists of the included studies. We finally included five studies satisfying our inclusion criteria. Four articles examined mammography while the rest investigated multigene testing. The existing breast cancer screening programmes were found to be cost-effective among urban Chinese women, but one study concluded that they might cause harm to women in rural areas. Contextual factors, such as data absence, urban-rural disparity, willingness-to-pay threshold, and model design, imposed barriers to cost-effectiveness analysis. Multigene testing was found to be cost-effective and has a promising population impact among all women with breast cancer in China. Future research should investigate the cost-effectiveness of screening and identifying breast cancer through precision medicine technologies, including genetic testing, genome sequencing, cascade testing, and the return of secondary findings.
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 | 0.015 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".