Bibliographic record
Abstract
Samuli Ripatti and colleagues1Ripatti S Tikkanen E Orho-Melander M et al.A multilocus genetic risk score for coronary heart disease: case-control and prospective cohort analyses.Lancet. 2010; 376: 1393-1400Summary Full Text Full Text PDF PubMed Scopus (411) Google Scholar examine 13 recently discovered genetic risk factors for coronary heart disease (CHD) to estimate the magnitude of risk they confer above and beyond traditionally established risk factors. Ripatti and colleagues conclude that a genetic risk score comprising these 13 single nucleotide polymorphisms (SNPs) was associated with a significant increase in the risk of prevalent and incident CHD in a subsample of individuals of European ancestry.Although this is an important question and the study is methodologically sound and represents the largest effort to date on this topic, it was very disappointing to see that, after millions, and perhaps billions, of dollars invested in genomic research over the past few years, there is so little to show for it. Although the score was associated with incident disease, it failed to improve risk discrimination. The fact that non-invasive, easily available, and often inexpensive traditional risk factors for CHD such as age, gender, or blood pressure outperform by a large margin a genetic risk score reaffirms the importance of comprehensive physical examination and medical history as the cornerstones of the diagnostic process for CHD.2Pryor DB Shaw L McCants CB et al.Value of the history and physical in identifying patients at increased risk for coronary artery disease.Ann Intern Med. 1993; 118: 81-90Crossref PubMed Scopus (428) Google ScholarI declare that I have no conflicts of interest. Samuli Ripatti and colleagues1Ripatti S Tikkanen E Orho-Melander M et al.A multilocus genetic risk score for coronary heart disease: case-control and prospective cohort analyses.Lancet. 2010; 376: 1393-1400Summary Full Text Full Text PDF PubMed Scopus (411) Google Scholar examine 13 recently discovered genetic risk factors for coronary heart disease (CHD) to estimate the magnitude of risk they confer above and beyond traditionally established risk factors. Ripatti and colleagues conclude that a genetic risk score comprising these 13 single nucleotide polymorphisms (SNPs) was associated with a significant increase in the risk of prevalent and incident CHD in a subsample of individuals of European ancestry. Although this is an important question and the study is methodologically sound and represents the largest effort to date on this topic, it was very disappointing to see that, after millions, and perhaps billions, of dollars invested in genomic research over the past few years, there is so little to show for it. Although the score was associated with incident disease, it failed to improve risk discrimination. The fact that non-invasive, easily available, and often inexpensive traditional risk factors for CHD such as age, gender, or blood pressure outperform by a large margin a genetic risk score reaffirms the importance of comprehensive physical examination and medical history as the cornerstones of the diagnostic process for CHD.2Pryor DB Shaw L McCants CB et al.Value of the history and physical in identifying patients at increased risk for coronary artery disease.Ann Intern Med. 1993; 118: 81-90Crossref PubMed Scopus (428) Google Scholar I declare that I have no conflicts of interest. SNPs and coronary heart disease – Authors' replyThe main aim of our study was to validate recently discovered genetic risk factors for coronary heart disease (CHD) and to estimate the magnitude of risk conferred by these genetic risk factors in population-based prospective cohort studies. We showed that the joint effect of 13 known genetic loci—when measured as relative risk between the top and bottom 20% of individuals—was 1·7 (95% CI 1·4–2·0), even after adjusting for known Framingham risk factors1 and family history of CHD. The effect is comparable to that of systolic blood pressure (hazard ratio 1·7, 95% CI 1·2–2·3) but slightly smaller than for LDL cholesterol (2·1, 1·6–2·8). Full-Text PDF
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".