Cost-effectiveness of Breast Cancer Screening from 2013-2022: A Bibliometric Study
Bibliographic record
Abstract
Background: Breast cancer in women is the most common cancer case worldwide. Research on the cost-effectiveness of breast cancer screening still needs to be studied in depth and broadly . This study aims to show the development of research on the most effective and efficient breast cancer screening ( 2013-2022 ) . Methods: This is a qualitative literature study that used Scopus database . K eywords:(Breast Cancer) AND (Screening)) AND (Cost Effectiveness). 459 articles can be obtained from a total of 2.108. Subject area is related to medical scope, the document type is an article and journal in English language. This research uses VOS viewer and R-Studio for analysis. Result: The number of publications on this topic are fluctuating, the most publications in 2021 (61 articles). The 3 most authors are De Koning HJ, Van Reverstyn NT and Algoz O , affiliated with University of Toronto, University of Manchester and Harvard Medical School. Value In Health journals contributed the most publications . The most keywords: Female by 622 times. After 2021 the most discussed themes relate to human tissue, willingness to pay, and clinical outcomes. Conclusion: It is necessary to involve many LMICs in this topic research to obtain more applicable screening methods and modalities in countries with limited health facilities. The scope of research that is still open includes CEA research in LMIC, breast cancer in men, the quality of life of breast cancer patients, patient outcomes, genetic screening - testing and also gene expression profiling.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | high |
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.019 | 0.046 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".