Cardiovascular Outcomes and Renal Disease
Bibliographic record
Abstract
Letters16 April 2002Cardiovascular Outcomes and Renal DiseaseJohannes F.E. Mann, MD, Hertzel C. Gerstein, MD, and Salim Yusuf, MDJohannes F.E. Mann, MDThe HOPE Office; McMaster University; Hamilton, Ontario L8L 2X2, Canada (Mann, Gerstein, Yusuf)Search for more papers by this author, Hertzel C. Gerstein, MDThe HOPE Office; McMaster University; Hamilton, Ontario L8L 2X2, Canada (Mann, Gerstein, Yusuf)Search for more papers by this author, and Salim Yusuf, MDThe HOPE Office; McMaster University; Hamilton, Ontario L8L 2X2, Canada (Mann, Gerstein, Yusuf)Search for more papers by this authorAuthor, Article, and Disclosure Informationhttps://doi.org/10.7326/0003-4819-136-8-200204160-00021 SectionsAboutVisual AbstractPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinkedInRedditEmail IN RESPONSE:McCullough and colleagues correctly point out that patients with even mild renal insufficiency exhibit an excess of car-diovascular risk factors. Controlling for such risk factors indicated that renal insufficiency is an additional, independent risk factor. This is also evident from Table 1 of our article. Therapeutic nihilism was obviously not a problem of the HOPE study because antiplatelet, blood pressure-lowering, and cholesterol-lowering agents were, if anything, more frequently administered in patients with renal insuf-ficiency than those without.However, we emphasize that the results of the HOPE study contradict the common practice to withhold angiotensin-converting enzyme inhibitors, including ramipril, in patients with renal insuffi-ciency. We have no evidence that patients with renal insufficiency in the HOPE study received fewer thrombolytics; invasive procedures, including revascularization (a secondary outcome of the HOPE study); or β-blockers. It is entirely possible that risk factors we did not evaluate may explain some of the increased risk associated with even mild renal insufficiency. However, current experimental and clinical evidence indicates that some aspects of renal failure may promote atherosclerosis and may be treatable. Further research into these factors is necessary, and our article was published to stimulate such investigations. Comments0 CommentsSign In to Submit A Comment Author, Article, and Disclosure InformationAffiliations: The HOPE Office; McMaster University; Hamilton, Ontario L8L 2X2, Canada (Mann, Gerstein, Yusuf) PreviousarticleNextarticle Advertisement FiguresReferencesRelatedDetailsSee AlsoRenal Insufficiency as a Predictor of Cardiovascular Outcomes and the Impact of Ramipril: The HOPE Randomized Trial Johannes F.E. Mann , Hertzel C. Gerstein , Janice Pogue , Jackie Bosch , Salim Yusuf , and Cardiovascular Outcomes and Renal Disease Peter A. McCullough , Keisha R. Sandberg , and Steven Borzak Metrics 16 April 2002Volume 136, Issue 8Page: 634KeywordsAngiotensin converting enzyme inhibitorAntiplatelet therapyAtherosclerosisBloodCardiovascular disease riskMedical risk factorsRenal diseasesRenal failureRevascularization 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 | 0.002 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.051 | 0.009 |
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".