Notice bibliographique
Résumé
BACKGROUND: Atherosclerosis of coronary arteries is the most common cause of myocardial infarction (MI), which is initiated from childhood and progresses gradually by aging. Several risk factors influence its progress, and are categorized as classic, traditional and novel factors. The role of unknown risk factors is becoming increasingly more significant recently. The aim of this study is to underscore the novel risk factors despite the importance of classic factors and consider these factors for future studies. METHODS: This is a prospective study on 180 myocardial infarction cases, conducted in the cardiology ward and CCU of Imam-Reza hospital (Mashad-IRAN). A number of risk factors identified and evaluated in these patients included: hyperlipidemia, hypertension, diabetes, smoking, activity, stress, hair of external ear canal and ear lobe crease, age, and sex. Then patients without any risk factor or with one or two risk factors were distinguished. RESULTS: The majority of our patients were old men in the age range of 60 - 69 years. Amongst all patients 42.2% were smokers, 68.3% were type A personality group, 19% were active, 81% were physically inactive, 37.2% had hairy ear canal, 35% had hypertension, 21.1% were diabetic, 14.4% had hyperlipidemia and 30% had positive family history of myocardial infarction. Of great interest was the fact that of the patients whose case was studied, many did not have any risk factor or in some cases had only one. CONCLUSIONS: In regard of increasing rate of cardiovascular diseases and myocardial infarction even amongst the young population, and because of considerable need to improve vascular risk detection, much research over the past decade has focused on identification of novel atherosclerotic risk factors, and some of these new risk factors are identified and some may be unknown. Amongst the new risk factors, inflammation has an important role, other risk factors that must be assessed are homocysteine, serum amyloid, and antibodies against Oxidized LDL. So we recommend that governments and heart associations must introduce new plans and policies in order to tackle the problem and reduce the frequency of cardiovascular disease. This requires the understanding of the conventional or classic risk factors and also the less known and new risk factors and ways which they may be prevented.
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 | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
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 ».