Long-Term Prediction of Coronary Heart Disease in Young Men
Bibliographic record
Abstract
Letters16 April 2002Long-Term Prediction of Coronary Heart Disease in Young MenApoor S. Gami, MN, Victor M. Montori, MD, and Steven A. Smith, MDApoor S. Gami, MNMayo Clinic; Rochester, MN 55905 (Gami, Montori, Smith)Search for more papers by this author, Victor M. Montori, MDMayo Clinic; Rochester, MN 55905 (Gami, Montori, Smith)Search for more papers by this author, and Steven A. Smith, MDMayo Clinic; Rochester, MN 55905 (Gami, Montori, Smith)Search for more papers by this authorAuthor, Article, and Disclosure Informationhttps://doi.org/10.7326/0003-4819-136-8-200204160-00016 SectionsAboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinkedInRedditEmail TO THE EDITOR:Navas-Nacher and colleagues (1) discussed the impact of risk factors (age, serum cholesterol level, systolic blood pressure, and cigarette smoking) for coronary heart disease (CHD) on men 18 to 39 years of age. They found a significant association between these risk factors and death from CHD over 20 years.The authors described measuring plasma glucose levels in the 11 016 participants. However, they did not report plasma glucose levels at baseline and apparently did not explore the association between glycemia and cardiovascular mortality. We would like to know whether a significant association existed between plasma glucose levels ...References1. Navas-Nacher EL, Colangelo L, Beam C, Greenland P. Risk factors for coronary heart disease in men 18 to 39 years of age. Ann Intern Med. 2001;134:433-9. [PMID: 11255518] LinkGoogle Scholar2. Fuller JH, Shipley MJ, Rose G, Jarrett RJ, Keen H. Mortality from coronary heart disease and stroke in relation to degree of glycaemia: the Whitehall study. Br Med J Clin Res Ed. 1983;287:867-70. [PMID: 6412862] CrossrefMedlineGoogle Scholar3. Castelli WP. Cardiovascular disease in women. Am J Obstet Gynecol. 1988;158:1553-60, 1566-7. [PMID: 3377033] CrossrefMedlineGoogle Scholar4. Coutinho M, Gerstein HC, Wang Y, Yusuf S. The relationship between glucose and incident cardiovascular events. A metaregression analysis of published data from 20 studies of 95,783 individuals followed for 12.4 years. Diabetes Care. 1999;22:233-40. [PMID: 10333939] CrossrefMedlineGoogle Scholar5. Khaw KT, Wareham N, Luben R, Bingham S, Oakes S, Welch A, et al . Glycated haemoglobin, diabetes, and mortality in men in Norfolk cohort of european prospective investigation of cancer and nutrition (EPIC-Norfolk). BMJ. 2001;322:15-8. [PMID: 11141143] CrossrefMedlineGoogle Scholar Author, Article, and Disclosure InformationAffiliations: Mayo Clinic; Rochester, MN 55905 (Gami, Montori, Smith) PreviousarticleNextarticle Advertisement FiguresReferencesRelatedDetailsSee AlsoRisk Factors for Coronary Heart Disease in Men 18 to 39 Years of Age Elena L. Navas-Nacher , Laura Colangelo , Craig Beam , and Philip Greenland Long-Term Prediction of Coronary Heart Disease in Young Men Philip Greenland and Laura Colangelo Metrics 16 April 2002Volume 136, Issue 8Page: 631KeywordsBlood plasmaCholesterolCohort studiesCongenital heart diseaseCoronary heart diseaseGlucoseHemoglobinMedical risk factorsMortalitySystolic pressure ePublished: 16 April 2002 Issue Published: 16 April 2002 CopyrightCopyright © 2002 by American College of Physicians. All Rights Reserved.PDF DownloadLoading ...
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".