Bibliographic record
Abstract
Should we start lipid-lowering treatment earlier in life? If yes, when? In a nutshell, this is the fundamental question raised by Pirillo and Catapano in their excellent editorial.1 Responding to this question is critical to shaping the cardiovascular disease (CVD) preventive strategy. Following the distinction made by Geoffrey Rose, there are two types of CVD preventive strategies, the high-risk and the population-based.2,3 The high-risk strategy is the backbone of clinical prevention and consists of identifying and treating individuals with a high absolute risk of CVD. Multiple methods have been proposed to estimate absolute CVD risk and identify these high-risk individuals. Because age is a major component of the absolute CVD risk level, most eligible patients are relatively old. Within a life course epidemiology perspective, there is however large evidence that the effect of CVD risk factors such as LDL-C or blood pressure can be traced back to young adulthood and childhood, giving arguments to account for risk accumulated early in life and for starting treatment early in life.4 An early-life high-risk preventive strategy is certainly appealing from a physio-pathological point of view. However, several issues make this strategy inefficient. First, the discriminative power of LDL-C or blood pressure for identifying individuals who will suffer or not from a CVD is notoriously weak among adults,5 and it is weaker at a younger age. Many clinicians still struggle to acknowledge this discriminative power weakness which reduces the fundamental informative value of blood lipid or blood pressure screening to decide whether to treat or not.5 Second, treating individuals earlier in life will lead to what is called a pseudo-high-risk preventive strategy, widening the number of people eligible for treatment whose probability of having a CVD is negligible before a long time, making any benefit impossible before decades of treatment.3 Third, the cumulative risk of a lifetime exposure to a relatively high level of LDL-C or blood pressure must be balanced against the cumulative risk of a lifetime exposure to a treatment. If treatments have any non-negligible clinical potential adverse effects, the balance will not be in favour of starting treatment early in life. Finally, treating patients over decades implies follow-up, incurs costs, and necessitates care workforces, and that is not sustainable in most healthcare settings. Applying an individual CVD risk-based preventive approach will always be frustrating: either you lower the absolute risk level and treat a large number of people for decades, without benefit for most of them, either you keep a relatively high level of risk and fail to prevent the majority of preventable cases;2 that is a recall of the fundamental limitations of the clinical risk-based CVD preventive strategy. To limit the ever-expansion of a pseudo-high-risk strategy, we must strengthen a population-based approach towards the primordial prevention of CVD, starting early in life.6 Swiss National Science Foundation (SNSF) grant 188549.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.031 | 0.007 |
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".