Family History: Impact on Coronary Heart Disease Risk Assessment beyond Guideline-Defined Factors
Bibliographic record
Abstract
OBJECTIVE: Family history (FH) provides insights into the effects of shared genomic susceptibilities, environments and behaviors, making it a potentially valuable risk assessment tool for chronic diseases. We assessed whether coronary heart disease (CHD) risk assessment is improved when FH information is added to other clinical information recommended in guidelines. METHODS: We applied logistic regression analyses to cross-sectional data originally obtained from a UK study of women who delivered a live-born infant between 1951 and 1970. We developed 3 models: Model 1 included only the covariates in a guideline applicable to the population, Model 2 added FH to Model 1, and Model 3 included a fuller range of risk factors. For each model, its ability to discriminate between study subjects with and those without CHD was evaluated and its impact on risk classification examined using the net reclassification index. RESULTS: FH was an independent risk factor for CHD (odds ratio = 1.7, 95% confidence interval = 1.26-2.47) and improved discrimination beyond guideline-defined clinical factors (p < 0.0006). However, the difference in the area under the curve of 2.8% and the extent of patient reclassification resulting from the inclusion of FH were small (p = 0.11). CONCLUSION: While FH were a significant independent risk factor for CHD, it added little to risk factors typically included in guidelines.
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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 | 0.017 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".