Is Treatment Inertia Unique to Publicly Funded Healthcare? Insights from the Guidelines Oriented Approach to Lipid lowering (GOAL) Canada Program
Bibliographic record
Abstract
Background: We compared the use of lipid lowering therapy, low density-lipoprotein cholesterol (LDL-C) levels, and proportion achieving guideline-recommended LDL-C levels in patients with private vs. public insurance coverage for their lipid lowering treatment. Materials and Methods: Guidelines Oriented Approach to Lipid lowering (GOAL) Canada enrolled 2009 patients with cardiovascular disease (CVD) or heterozygous familial hypercholesterolemia (FH) and an LDL-C above the guideline-recommended target of <2.0 mmol/L despite maximally tolerated statin therapy. During two follow-up visits physicians received online reminders of treatment recommendations. Results: Of 2009 patients enrolled (median age 63 years, 42% female), there were 1284 (64%) patients with private and 725 (36%) with public insurance for lipid lowering therapy. Patients with private insurance were younger and less likely to have a history of heart failure or to be on bile acid sequestrants. There was no difference between the groups in their lipid levels or lipid lowering therapy at baseline. During the follow up, there was no difference in the use of ezetimibe; however, the use of PCSK9i was more frequent in patients with private insurance (31.7 % vs. 21%, p<0.0001), the mean LDL-C level was slightly lower (2.11±1.17 vs. 2.31±1.17 mmol/L, p = 0.001), and the proportion of patients achieving the guideline-recommended LDL-C level was greater (54% vs. 45.5%, p = 0.001). After adjustment for other factors in a multivariable model, private insurance was not a significant predictor of achieving the guideline-recommended LDL-C level in a multivariable model. Conclusion: While PCSK9i use was higher in patients with private insurance, the majority of patients with either private or public insurance experienced similar treatment inertia. The cost of non-generic medications does not appear to be the dominant reason for the continued care gap in lipid lowering of high-risk patients.
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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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".