Relationship between remnant cholesterol and risk of heart failure in participants with diabetes mellitus: Reply
Bibliographic record
Abstract
Journal Article Relationship between remnant cholesterol and risk of heart failure in participants with diabetes mellitus: Reply Get access Ruoting Wang, Ruoting Wang Center for Clinical Epidemiology and Methodology (CCEM), Guangdong Second Provincial General Hospital, Guangzhou, China Search for other works by this author on: Oxford Academic Google Scholar Hertzel C Gerstein, Hertzel C Gerstein Department of Medicine, McMaster University, Hamilton, ON, CanadaPopulation Health Research Institute, McMaster University, Hamilton, ONCanada https://orcid.org/0000-0001-8072-2836 Search for other works by this author on: Oxford Academic Google Scholar Harriette G C Van Spall, Harriette G C Van Spall Department of Medicine, McMaster University, Hamilton, ON, CanadaPopulation Health Research Institute, McMaster University, Hamilton, ONCanada https://orcid.org/0000-0002-8370-4569 Search for other works by this author on: Oxford Academic Google Scholar Gregory Y H Lip, Gregory Y H Lip Liverpool Centre for Cardiovascular Science at University of Liverpool, Liverpool John Moores University and Liverpool Heart & Chest Hospital, Liverpool, United KingdomDepartment of Clinical Medicine, Aalborg University, Aalborg, Denmark https://orcid.org/0000-0002-7566-1626 Search for other works by this author on: Oxford Academic Google Scholar Ivan Olier, Ivan Olier Liverpool Centre for Cardiovascular Science at University of Liverpool, Liverpool John Moores University and Liverpool Heart & Chest Hospital, Liverpool, United KingdomSchool of Computer Science and Mathematics, Liverpool John Moores University, Liverpool, United Kingdom https://orcid.org/0000-0002-5679-7501 Search for other works by this author on: Oxford Academic Google Scholar Sandra Ortega-Martorell, Sandra Ortega-Martorell Liverpool Centre for Cardiovascular Science at University of Liverpool, Liverpool John Moores University and Liverpool Heart & Chest Hospital, Liverpool, United KingdomSchool of Computer Science and Mathematics, Liverpool John Moores University, Liverpool, United Kingdom https://orcid.org/0000-0001-9927-3209 Search for other works by this author on: Oxford Academic Google Scholar Lehana Thabane, Lehana Thabane Father Sean O'Sullivan Research Centre, St. Joseph's Healthcare Hamilton, Hamilton, ON, CanadaDepartment of Health Research Methods, Evidence, and Impact (HEI), McMaster University, Hamilton, ON, Canada https://orcid.org/0000-0003-0355-9734 Search for other works by this author on: Oxford Academic Google Scholar Zebing Ye, Zebing Ye Department of Cardiology, Guangdong Second Provincial General Hospital, Guangzhou, China Search for other works by this author on: Oxford Academic Google Scholar Guowei Li Guowei Li Center for Clinical Epidemiology and Methodology (CCEM), Guangdong Second Provincial General Hospital, Guangzhou, ChinaFather Sean O'Sullivan Research Centre, St. Joseph's Healthcare Hamilton, Hamilton, ON, CanadaDepartment of Health Research Methods, Evidence, and Impact (HEI), McMaster University, Hamilton, ON, Canada Corresponding author. Tel: 86-020-32640264; Fax: 86-020-89169025, Email: lig28@mcmaster.ca Search for other works by this author on: Oxford Academic Google Scholar European Heart Journal - Quality of Care and Clinical Outcomes, Volume 9, Issue 5, August 2023, Page 547, https://doi.org/10.1093/ehjqcco/qcad038 Published: 12 July 2023 Article history Received: 26 June 2023 Accepted: 29 June 2023 Corrected and typeset: 12 July 2023 Published: 12 July 2023
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How this classification was reachedexpand
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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".