Editorial: Epidemiologic Reviews 2013--Special Issue on the Epidemiology of Aging
Notice bibliographique
Résumé
The papers in this volume (1–13) reflect substantial and diverse epidemiologic research activity on some of the major health problems of older people. When the call for papers was announced, no particular themes within this vast field of the epidemiology of aging were suggested or encouraged. This led to a series of reviews covering a breadth of modern and important topics. These topics touch many disciplines that dovetail with the science of aging, including evaluating putative social and biological risk factors for age-related change and survivorship, identifying biological processes and modern biomarkers of aging, and exploring aging outcomes in older populations, such as the issue of multimorbidity among older Americans and military veterans in Canada and other countries. Several papers review risk factors for altered cognitive function and dementia-related illnesses. Although firm conclusions are not always possible given the state of the science, important directions for moving forward are offered. The preventive potential of these works is always close to the surface. The papers also encompass some of the general themes and challenges of population aging research. The importance of life-course concepts in the epidemiology of aging is present in several reports. For example, the report by Dahl and Hassing (2) finds an association of midlife obesity and late-life cognitive function. Other reports also address the problem of accurately identifying early life exposures that predict late-life age-related changes. The incorporation of modern biological indicators of aging and survivorship, such as genetic and genomic factors (13) and telomere length (9), reflects the importance of molecular approaches to the long-standing quest for biomarkers of the progression of age-related change, biomarkers that are precise and robust across populations. As these biomarkers are validated, they will take their places as useful prognostic factors for personalized medicine. However, to be useful, such biomarkers need to be generally independent of untoward environmental exposures and the variety of diseases that are prevalent in older populations, a common problem noted in the report by Salive (6). Other reports in this volume denote the great variation in health and function among older populations, highlighting the challenges for defining preventive approaches for older persons that can reach large segments of this very heterogeneous group. Although new methodological approaches are always welcome, collectively this collection of review papers bespeaks the active international activity in population aging and points to directions in understanding aging mechanisms; defining potential biological, clinical, and social intervention studies to enhance successful aging; and providing more targeted, efficient, and effective approaches to delivering health services to older persons. The epidemiology of aging is alive and well!
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 | 0,012 | 0,039 |
| Méta-épidémiologie (sens strict) | 0,008 | 0,002 |
| Méta-épidémiologie (sens large) | 0,007 | 0,006 |
| Bibliométrie | 0,009 | 0,004 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,010 | 0,006 |
| Science ouverte | 0,006 | 0,002 |
| Intégrité de la recherche | 0,015 | 0,016 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,053 | 0,035 |
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 ».