Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A preventive strategy of cardiovascular disease is the identification and treatment of high-risk individuals.1Rose G Strategy of preventive medicine. Oxford University Press, Oxford2008Crossref Scopus (190) Google Scholar, 2Chiolero A Paradis G Paccaud F The pseudo-high-risk prevention strategy.Int J Epidemiol. 2015; 44: 1469-1473Crossref PubMed Scopus (20) Google Scholar One major challenge with this strategy is that it requires tools to discriminate high-risk individuals from other individuals by appropriate screening tests and stratification methods. Furthermore, once individuals have been categorised by risk, it might seem that everything has been decided: high-risk individuals should be treated whereas others should not. However, there follows another major issue: should patients initially not categorised at high risk be rescreened? And, if yes, in which time interval? In The Lancet Public Health, Joni Lindbohm and colleagues help to address this question. Using data on 6964 individuals followed up for a mean of 22·0 years (SD 5·0) with biomedical measurements taken at 5-year intervals, the authors estimated the optimal screening intervals for cardiovascular disease risk based on progression rates from low-risk and intermediate-risk categories to the high-risk category.3Lindbohm JV Sipilä PN Mars NJ et al.Optimal screening intervals for cardiovascular disease prevention: a cohort study.Lancet Public Health. 2019; 4: e189-e199Summary Full Text Full Text PDF Scopus (13) Google Scholar They concluded that the commonly recommended 5-year screening intervals to detect individuals at high risk of major cardiovascular events are unnecessarily frequent for low-risk individuals and insufficiently frequent for intermediate-risk individuals. On the basis of their analyses, they propose to tailor screening intervals according to initial risk estimates—that is, 7 years for low-risk individuals, 4 years for intermediate-low-risk individuals, and 1 year for intermediate-high-risk individuals. Their model suggests that such a strategy would improve prevention of major cardiovascular events without raising health-care costs. This study suggests that a one-size-fits-all screening interval is not optimal and that personalised risk-based screening intervals could be more efficient for the prevention of cardiovascular disease. Conceptually, the idea is very simple: patients' information on cardiovascular disease risk gathered at each screening is used to tailor the interval until the subsequent screening. With this information, prediction of cardiovascular disease risk progression is improved, hence allowing shortening or lengthening of the time until the next screening depending on the expected speed of change in risk category. Surprisingly, very few studies have been designed to determine optimal screening intervals for cardiovascular disease risk and related risk factors, including blood lipid or blood pressure.4McKinn S Bonner C Jansen J et al.Factors influencing general practitioners' decisions about cardiovascular disease risk reassessment: findings from experimental and interview studies.BMC Fam Pract. 2016; 17: 107Crossref PubMed Scopus (2) Google Scholar For instance, blood lipids are measured at an initial screening visit: if the level is satisfactory, no treatment is initiated, but because blood lipid tends to increase with age, rescreening will eventually be necessary. The question is how often should rescreening take place? Annually? Less or more frequently? A major challenge is to account for random variability inherent to the individual, which makes it difficult to identify long-term changes in lipid (or risk) level—ie, the signal upon which the decision to intervene is based—given the short-term within-person variation—ie, the noise.5Glasziou PP Irwig L Heritier S Simes RJ Tonkin A LIPID Study InvestigatorsMonitoring cholesterol levels: measurement error or true change?.Ann Intern Med. 2008; 148: 656-661Crossref PubMed Scopus (101) Google Scholar, 6Hutcheon JA Chiolero A Hanley JA Random measurement error and regression dilution bias.BMJ. 2010; 340: c2289Crossref PubMed Scopus (453) Google Scholar Hence, in the absence of treatment, most differences in blood lipid readings within a 3-year period have been shown to be due to random biological variability or measurement error.7Perera R McFadden E McLellan J et al.Optimal strategies for monitoring lipid levels in patients at risk or with cardiovascular disease: a systematic review with statistical and cost-effectiveness modelling.Health Technol Assess. 2015; 19: 1-401Crossref Scopus (24) Google Scholar Based on this finding, the ideal blood lipid rescreening interval among untreated patients could be at least 3 years, a longer interval than usual practice.4McKinn S Bonner C Jansen J et al.Factors influencing general practitioners' decisions about cardiovascular disease risk reassessment: findings from experimental and interview studies.BMC Fam Pract. 2016; 17: 107Crossref PubMed Scopus (2) Google Scholar For hypertension, the standard is routine blood pressure screening at every visit, regardless of patient complaint, previous measures, or the interval since the last measures; this blind, uninformed approach is simple but surely not efficient, calling for a more informed, data-driven, screening strategy.8Garrison GM Oberhelman S Screening for hypertension annually compared with current practice.Ann Fam Med. 2013; 11: 116-121Crossref PubMed Scopus (10) Google Scholar One study suggests that the optimal interval could be 3 years or more for patients with systolic blood pressure less than 130 mm Hg and 2 years for those with systolic blood pressure of at least 130 mmHg;9Takahashi O Glasziou PP Perera R Shimbo T Fukui T Blood pressure re-screening for healthy adults: what is the best measure and interval?.J Hum Hypertens. 2012; 26: 540-546Crossref PubMed Scopus (14) Google Scholar this is a risk-based strategy to tailor screening intervals. In practice, many physicians tend to screen too often for cardiovascular disease risk.4McKinn S Bonner C Jansen J et al.Factors influencing general practitioners' decisions about cardiovascular disease risk reassessment: findings from experimental and interview studies.BMC Fam Pract. 2016; 17: 107Crossref PubMed Scopus (2) Google Scholar, 10Bell KJ Hayen A Irwig L Takahashi O Ohde S Glasziou P When to remeasure cardiovascular risk in untreated people at low and intermediate risk: observational study.BMJ. 2013; 346: f1895Crossref PubMed Scopus (19) Google Scholar Although personalising cardiovascular disease risk screening intervals, as suggested by Lindbohm and colleagues, is very appealing, it is complex because it requires adequate tools to estimate cardiovascular disease risk at the point of care and an efficient information system to record this risk and track its progression over the life course. This process seems difficult to implement in most clinical settings. The growing use of electronic health records, coupled with appropriate algorithm and cardiovascular disease risk estimators, will surely open new avenues in this field. Meanwhile, we have to keep in mind that the public health issues at stakes are huge as the burden and cost of screening and lifelong monitoring of cardiovascular disease or other chronic diseases and related risk factors is growing exponentially in ageing populations. We declare no competing interests. 5-year versus risk-category-specific screening intervals for cardiovascular disease prevention: a cohort studyIn terms of timely preventive interventions, the 5-year screening intervals were unnecessarily frequent for low-risk individuals and insufficiently frequent for intermediate-risk individuals. Screening intervals based on risk-category-specific progression rates would perform better in terms of preventing major cardiovascular disease events and improving cost-effectiveness. Full-Text PDF Open Access
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.042 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.006 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it