Breast cancer risk assessment: Evaluation of screening tools for genetics referral
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The United States Preventative Services Task Force (USPSTF) recommends breast cancer risk-screening tools to help primary care providers determine which unaffected patients to refer to genetic specialists. The USPSTF does not recommend one tool above others. The purpose of this study was to compare tool performance in identifying women at risk for breast cancer. METHODS: Pedigrees of 85 women aged 40-74 years with first-degree female relative with breast cancer were evaluated using five tools: Family History Screen-7 (FHS-7), Pedigree Assessment Tool, Manchester Scoring System, Referral Screening Tool, and Ontario Family History Assessment Tool (Ontario-FHAT). Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated to describe each tool's ability to identify women with elevated risk as defined by Claus Model calculations (lifetime risk ≥15%). Receiver operating curves were plotted. Differences between areas under the curve were estimated and compared through logistic regression to assess for differences in tool performance. CONCLUSIONS: Claus calculations identified 14 of 85 women with elevated risk. Two tools, Ontario-FHAT and FHS-7, identified all women with elevated risk (sensitivity 100%). The FHS-7 tool flagged all participants (specificity 0%). The Ontario-FHAT flagged 59 participants as needing referral (specificity 36.2%) and had a NPV of 100%. Area under the curve values were not significantly different between tools (all p values > .05), and thus were not helpful in discriminating between the tools. IMPLICATIONS FOR PRACTICE: The Ontario-FHAT outperformed other tools in sensitivity and NPV; however, low specificity and PPV must be balanced against these findings. Thus, the Ontario-FHAT can help determine which women would benefit from referral to genetics specialists.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".