Prevalence of clinical familial hypercholesterolaemia among patients with high cholesterol levels.
Bibliographic record
Abstract
INTRODUCTION: Familial hypercholesterolaemia (FH) can be diagnosed using clinical criteria or by direct mutation identification. The prevalence of clinical FH in Danish lipid clinics remains unknown. The objective of this study was to explore the prevalence of clinical FH in patients admitted on suspicion of FH with plasma low-density lipoprotein cholesterol (LDL-C) concentration ≥ 5.0 mmol/l. METHODS: We reviewed the medical records of 653 patients consecutively (from 1 January 2013 to 1 May 2017) referred to the lipid clinic at Viborg Regional Hospital, Denmark. Patients with LDL-C concentration > 4.9 mmol/l were selected. Clinical FH was assessed using the Dutch Lipid Clinic Network (DLCN) and Simon Broome criteria. RESULTS: Using DLCN, 315 patients (median 82% (95% confidence interval (CI): 78-86%)) had possible FH, 33 patients (median 9% (95% CI: 6-11%)) had probable FH and 36 patients (median 9% (95% CI: 6-12%)) had definite FH. Thus, a total of 69 patients (median 18% (95% CI: 14-22%)) had probable/definite FH. Using the Simon Broome criteria, 284 (median 74% (95% CI: 70-78%)) patients did not have FH, 67 patients (median 17% (95% CI: 14-21%)) had possible FH and 33 patients (median 9% (95% CI; 6-11%)) had definite FH, resulting in a total of 100 (median 26% (95% CI: 22-30%)) patients having possible/definite FH. The concordance between DLCN and Simon Broome FH was high among patients with definite FH (> 90%), but low among patients with probable or possible FH. CONCLUSIONS: Clinical FH was common among patients with LDL-C concentration ≥ 5.0 mmol/l referred to a Danish lipid clinic. However, the concordance between the DLCN and the Simon Broome criteria was low in a specialised clinical setting. FUNDING: The study was supported by a SANOFI grant. TRIAL REGISTRATION: not relevant.
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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 | 0.000 | 0.002 |
| 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) | 0.002 | 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".