Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,009 | 0,041 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,004 | 0,004 |
| Science ouverte | 0,001 | 0,004 |
| Intégrité de la recherche | 0,004 | 0,007 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,031 | 0,007 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».