Foreword
Notice bibliographique
Résumé
Cardiovascular disease (CVD) represents a substantial public health burden in Europe and there is a pressing need to implement comprehensive strategies to address this growing epidemic. To this purpose, surveillance remains the primary tool to evaluate the burden of disease, to plan preventive actions at both population and individual levels and to assess efficacy of prevention. Public health surveillance has been defined as ‘the ongoing, systematic collection, analysis, interpretation and dissemination of data regarding health-related events for use in public health action to reduce mortality and morbidity and to improve health’ (MMWR Recomm Rep 2001; 50:1-35.) Though CVD has been identified as one of the leading contributors to the global disease burden, the number of reliable and standardised indicators for which CVD data are available on a comparable basis across Europe is currently limited. Mortality data from EUROSTAT, WHO and OECD are only available for groups of diseases such as ischaemic heart disease (IHD) or cerebrovascular accidents (CVA). Hospital discharge data have been published using a variety of codes and classifications. In recent years, thanks to information technology, a substantial volume of data is being recorded on hospital admissions and discharges, medication use, in-patient care utilisation, surgical operations and invasive procedures. These data, provided that they are properly linked and validated, can be important sources of information for achieving better knowledge and more effective interventions, studying disease trends, producing annual reports, orientating preventive actions and making comparisons among countries. The EUROCISS project, funded by the European Commission, aimed to prioritise the aspects of CVD of major interest in EU countries and to provide a list of recommended indicators and sources of information for monitoring CVD. The main objective was to prepare the Manuals of Operations for the implementation of population-based registers of Acute Myocardial Infarction/Acute Coronary Syndrome (AMI/ACS), stroke and of CVD surveys. These manuals provide simple and comparable tools to support and stimulate implementation of surveillance systems in those countries which lack them but collect routine data such as mortality and hospital discharge records (HDR). They recommend to start from a minimum data set and follow a stepwise procedure, thus providing a standardised model for an efficient implementation of a surveillance system at reasonable cost. A population-based register is the best data source for the surveillance of AMI/ACS and stroke morbidity and mortality. It considers both fatal and nonfatal events occurring in-and out-of hospital, thus providing estimates of key indicators such as attack and case fatality rates. Population surveys can further supplement the information collected from registers with additional details on socio-demographic characteristics, risk factors, physical/biological measurements and chronic conditions. Europe is now facing the challenge to implement preventive actions, identify persons in need of treatment, apply the European Guidelines for CVD Prevention in Clinical Practice and verify improving effectiveness. The development, testing and implementation of effective surveillance systems for CVD will produce reliable and comparable indicators, thus enabling policy makers to trace differences within and between countries and to make better decisions on planning and evaluation of prevention programs, healthcare delivery, resource allocation, and research.
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 | 0,002 | 0,012 |
| 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 | 0,002 | 0,001 |
| Communication savante | 0,004 | 0,002 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,006 | 0,007 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,200 | 0,204 |
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 ».