Sex Differences in the Presentation, Treatment, and Outcome of Patients With Familial Hypercholesterolemia
Bibliographic record
Abstract
eterozygous familial hypercholesterolemia (FH) is the most common genetic disorder with a prevalence of 1 in 311 and is associated with a high risk of premature cardiovascular disease (CVD).As an autosomal dominant condition, it affects men and women equally.However, little is known about sex-specific differences in prescription of lipidlowering therapy (LLT) and response to therapy in patients with FH. 1,2 The objective of this study was to investigate sex-related differences in the presentation, treatment, response, and clinical outcomes of patients with HF.This study was approved by the Research Ethics Board of the Providence Health Care Research Institute.All patients provided written informed consent.We included participants within the British Columbia FH Registry with a diagnosis of "probable" or "definite" FH according to Dutch Lipid Clinic Network Criteria (DLCN) 3 .We examined baseline characteristics, LLT, attainment of lipid targets (defined as LDL-C<2.00 mmol/L or a>50% reduction from baseline) 4 , and CVD rates.Baseline was defined as the first available medical record, while follow-up was defined as the last clinic visit as of August 2019.Baseline characteristics, CVD rates, LLT prescription, and response were analysed using a 2-sample t-test and χ 2 test.Paired t-test, Wilcoxon matched-pairs signed rank test, and Mann-Whitney U-test were used to analyze LLT response as appropriate.The authors declare that all supporting data are available within this article.Women comprised 52.5% of our cohort and were diagnosed later than men (Table ), even after excluding
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".