Treatment Inertia in Patients With Familial Hypercholesterolemia
Bibliographic record
Abstract
Background We studied care gap in patients with familial hypercholesterolemia (FH) with respect to lipid‐lowering therapy. Methods and Results We enrolled patients with cardiovascular disease (CVD) or FH and low‐density lipoprotein‐cholesterol >2.0 mmol/L despite maximally tolerated statin therapy. During follow‐up physicians received online reminders of treatment recommendations of 2009 patients (median age, 63 years, 42% women), 52.4% had CVD only, 31.7% FH only, and 15.9% both CVD and FH. Patients with FH were younger and more likely to be women and non‐White with significantly higher baseline low‐density lipoprotein‐cholesterol level (mmol/L) as compared with patients with CVD (FH 3.92±1.48 versus CVD 2.96±0.94, P <0.0001). Patients with FH received less statin (70.6% versus 79.2%, P =0.0001) at baseline but not ezetimibe (28.1% versus 20.4%, P =0.0003). Among patients with FH only, 45.3% were at low‐density lipoprotein target (≥ 50% reduction from pre‐treatment level or low‐density lipoprotein <2.5 mmol/L) at baseline and increasing to 65.8% and 73.6% by visit 2 and 3, respectively. Among patients with CVD only, none were at recommended level (≤2.0 mmol/L) at baseline and 44.3% and 53.3% were at recommended level on second and third visit, respectively. When primary end point was analyzed as a difference between baseline and last available follow‐up observation, only 22.0% of patients with FH only achieved it as compared with 45.8% with CVD only ( P <0.0001) and 55.2% with both FH+CVD ( P <0.0001). Conclusions There is significant treatment inertia in patients with FH including those with CVD. Education focused on patients with FH should continue to be undertaken.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".