Abstract 17368: The Diagnosis of Heterozygous Familial Hypercholesterolemia: Genotype versus Phenotype
Bibliographic record
Abstract
Background: Heterozygous familial hypercholesterolemia (HeFH) is the most common dominantly-inherited disorder in man, affecting between 1:250 and 1:300 persons worldwide and estimates of more than 3 million with HeFH in the USA and Europe alone. Diagnosis is usually made by clinical criteria and confirmed with genetic testing. Despite next-generation gene sequencing, a causative mutation cannot be identified in up to 20% of patients even with a clinical diagnosis of definite HeFH. Given the prevalence, clinical diagnostic criteria are important tools for detection and early treatment of HeFH patients, particularly for clinicians outside of referral centers. Methods: The RUTHERFORD-1 and -2 studies enrolled patients with a clinical and/or genetic diagnosis of HeFH. All consenting patients in this analysis underwent FH genotyping. We compare the accuracy of clinical and genetic diagnosis of HeFH. Results: Of the 416 patients enrolled who consented to genetic testing, 342 (82%) were found to have genetically-confirmed HeFH. Of these patients, 288 (84%) met the Simon Broome criteria for definite and 54 (16%) for probable HeFH; 250 (75%) met the MEDPED clinical criteria for 100%, and 301 (90%) the 80% likelihood for a diagnosis of HeFH (Table). Conclusion: Although the optimal method to diagnose HeFH remains unclear, from these two trials there was close agreement between the two clinical classifications and genetic criteria. There were only a relatively small number of patients in whom genetic testing could not confirm the clinical diagnosis. Clinical criteria offer an inexpensive, low-technology ability for widespread screening in patients suspected of having HeFH. The advantage of genetic testing is that it allows for cascade testing of family members for the identified mutation who may not fulfill clinical criteria for the diagnosis of HeFH and application of family-oriented preventive strategies.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".