Establishing a multi-specialty consensus in the clinical need for hypercholesterolemia management and its implication for patients access to innovative therapies
Bibliographic record
Abstract
BACKGROUND: Increased level of blood LDL-C has a causal and cumulative effect on advancing atherosclerotic cardiovascular diseases (ASCVD). European guidelines for treating high LDL-C levels have been recently updated. However, in France, several challenges (e.g., physician and patient awareness, healthcare management) limit the application of management guidelines. The aim of this study was to understand the current opinions and perceived unmet clinical needs in recognising and managing hypercholesterolemia as an ASCVD risk factor, and to explore consensus around factors that support the effective management of elevated LDL-C. METHODS: An expert group of cardiologists, endocrinologists, biology/genetics researchers, and a health technology assessments expert, from France was convened. The current management of hypercholesterolemia and barriers to achieving LDL-C goals in France were discussed and 44 statements were developed. Wider consensus was assessed by sending the statements as a 4-point Likert Scale questionnaire to cardiologists and endocrinologists across France. The consensus threshold was defined as ≥75%. RESULTS: A total of 101 responses were received. Consensus was very high (>90%) in 25 (57%) statements, high (≥75%) in 18 (41%) statements and was not achieved (<75%) only in 1 (2%) of statements. Overall, 43 statements achieved consensus. CONCLUSIONS: Based on consensus levels, key recommendations for improving current guidelines and approaches to care have been developed. Implementation of these recommendations will lead to better concordance with international treatment guidelines and increase levels of education for healthcare practitioners and patients. In turn, this will improve the available treatment pathways for cardiovascular diseases, potentially creating improved patient outcomes in the future.
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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 | 0.105 | 0.099 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".