ArticleThe British journal of general practice : the journal of the Royal College of General Practitioners2003
A nationwide adaptive prediction tool for coronary heart disease prevention.
Article in The British journal of general practice : the journal of the Royal College of General Practitioners, 2003. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed.
- Automated electronic reminders to facilitate primary cardiovascular disease prevention: randomised controlled trial.The British journal of general practice : the journal of the Royal College of General Practitioners · 2010Trial
- Article
- Where now with clinical computer systems?The British journal of general practice : the journal of the Royal College of General Practitioners · 2003Article
- Delayed prescriptions in primary care.The British journal of general practice : the journal of the Royal College of General Practitioners · 2003Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Standardised electronic recording of cardiovascular risk factor data collected during primary care delivery could be used to create a new strategy, using an adaptive prediction model, for targeting primary prevention interventions at high-risk individuals. In the short term, this should progressively improve data quality and allow risk modification to be monitored at the population level. In the long term, feedback of data on cardiovascular disease development might enable the model to tailor the recommended interventions more appropriately to the needs of the individual and to adapt to future changes in risk patterns. Ultimately, the inclusion of additional cardiovascular risk factors might enable a richer, more realistic picture of cardiovascular risk profiles to be uncovered. This model may have wider uses in both research and practice, and provides a further incentive for the standardisation of record keeping in primary care.
Indexed as
Identifiers
14702907PMC1314730What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.