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Foreword

2007· article· en· W2739465416 on OpenAlexaff
Simona Giampaoli, Simon Capewell, Emer Shelley, Steven Allender, Andrew Briggs, Torben Jørgensen, Darwin R. Labarthe, Pedro Marques‐Vidal, Birgitta Stegmayr, W. M. Monique Verschuren, Tomasz Zdrojewski

Bibliographic record

VenueEuropean Journal of Cardiovascular Prevention & Rehabilitation · 2007
Typearticle
Languageen
Field
Topic
Canadian institutionsCanadian Association of Cardiovascular Prevention and Rehabilitation
Fundersnot available
KeywordsComputer science

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.200
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0040.002
Open science0.0010.002
Research integrity0.0060.007
Insufficient payload (model declined to judge)0.2000.204

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.

Opus teacher head0.013
GPT teacher head0.258
Teacher spread0.245 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEditorial

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".

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Citations0
Published2007
Admission routes1
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