Breast cancer risk assessment evaluation of screening tools for genetics referral for women in Taiwan
Bibliographic record
Abstract
BACKGROUND: Risk screening tools recommended by the United States Preventative Services Task Force (USPSTF) are used to screen potential BRCA1/2 pathogenic variant carriers. The purpose of this study was to identify an appropriate breast cancer risk-screening tool for genetic referral among women with a family history of breast cancer in Taiwan. METHODS: A cross-sectional design with convenience sampling was used in this study. Women with a family history of breast cancer but not diagnosed with breast cancer were recruited from surgical outpatient clinics. Sociodemographic and family cancer history were collected based on the screening tools. Both the Tyrer-Cuzick (IBIS) and BRCAPRO were used as a Gold standard to evaluate the accuracy of five screening tools. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and the areas under the receiver-operating curve (AUC) were compared to identify the most accurate one to determine women with elevated risk as defined by IBIS and BRCAPRO calculations with lifetime risk over 15%. RESULTS: One hundred twenty-four women with a family history of breast cancer but not yet diagnosed as breast cancer were recruited in this study. When the Tyrer-Cuzick (IBIS) was used as the standard, the AUC for the tools ranged from 0.490 to 0.562. When the BRCAPRO was used as the standard, the p values of the Ontario Family History Assessment Tool (Ontario-FHAT) (p = 0.003) and Pedigree Assessment Tool (PAT) (p = 0.016) were significant, and the AUCs were 0.938 and 0.854 for Ontario-FHAT and PAT, respectively. Since the sensitivity of Ontario-FHAT was 100, which is higher than PAT, we considered that using Ontario-FHAT in Taiwanese women would be better than using PAT. CONCLUSIONS: Ontario-FHAT would be an appropriate screening tool for identifying individuals in Taiwan who may need a genetic referral for further BRCA1/2 risk evaluation.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".