Lower risk of cardiovascular events and death associated with initiation of sodium‐glucose cotransporter‐2 inhibitors versus sulphonylureas: Analysis from the <scp>CVD‐REAL</scp> 2 study
Bibliographic record
Abstract
Su-Yen Goh has received institutional grants from AstraZeneca, Medtronic and Sanofi. She has participated in advisory boards for Amgen, AstraZeneca, Bayer, Boehringer Ingelheim, Novo Nordisk, Medtronic, MSD and Sanofi, and has received honoraria/speaker fees. Mikhail N. Kosiborod has received research grants from AstraZeneca and Boehringer Ingelheim, other research support from AstraZeneca, and has received honoraria from AstraZeneca, Boehringer Ingelheim and Novo Nordisk. He has acted as a consultant or participated on advisory boards for 35Pharma, Alnylam, Amgen, Applied Therapeutics, AstraZeneca, Bayer, Boehringer Ingelheim, Cytokinetics, Dexcom, Eli Lilly, Esperion Therapeutics, Janssen, Lexicon, Merck (Diabetes and Cardiovascular), Novo Nordisk, Pharmacosmos, Pfizer, Sanofi, Structure Therapeutics, Vifor Pharma and Youngene Therapeutics. Carolyn S. P. Lam has received research support from Boston Scientific, Bayer, Roche Diagnositics, AstraZeneca, Medtronic and Vifor Pharma, and has served as a consultant or on an Advisory Board/Steering Committee/Executive Committee for Boston Scientific, Bayer, Roche Diagnostics, AstraZeneca, Medtronic, Vifor Pharma, Novartis, Amgen, Merck, Janssen Research & Development LLC, Menarini, Boehringer Ingelheim, Novo Nordisk, Abbott Diagnostics, Corvia, Stealth BioTherapeutics, JanaCare, Biofourmis, Darma, Applied Therapeutics, MyoKardia, WebMD Global LLC, Radcliffe Group Ltd and Corpus. Matthew A. Cavender has received personal fees from AstraZeneca, Merck, Sanofi-Aventis, Chiesi and research support (non-salary) from Abbott Laboratories, AstraZeneca, GlaxoSmithKline, The Medicines Company, Merck and Takeda. Shun Kohsaka has received grants from Bayer Yakuhin and Daiichi Sankyo and consulting fees from Bayer Yakuhin, Bristol-Myers Squibb, and Pfizer. Anna Norhammar has received honoraria for lectures and advisory board meetings for AstraZeneca, Novo Nordisk, Boehringer Ingelheim and Lilly. Kåre I. Birkeland has received grants to his institution from AstraZeneca for this study and has given lectures and consulted for Novo Nordisk, Sanofi, Lilly, Boehringer Ingelheim and Merck Sharp & Dohme. Reinhard W. Holl reports grants to the University Hospital, Ulm, from AstraZeneca. Dídac Mauricio has received honoraria for lectures or consulting from AB-Biotics, Almirall, Amgen, Eli Lilly, Esteve, Ferrer, Janssen, Menarini, Merck Sharp & Dohme, Novo Nordisk and Sanofi. Navdeep Tangri has received consulting fees from Otsuka, Tricida and AstraZeneca. He has received research support from AstraZeneca, including for this work. His research programme is supported by the Canadian Institute for Health Research and Research Manitoba. Jonathan E. Shaw has received honoraria for advisory boards and lectures from AstraZeneca, Boehringer Ingelheim, Eli Lilly, Merck Sharp & Dohme, Mylan, Novartis, Novo Nordisk and Sanofi. Marcus Thuresson is an employee at Statisticon AB, for which AstraZeneca is a client. Peter Fenici is an AstraZeneca employee and holds stock options. Dae Jung Kim has received grant support from Boehringer Ingelheim, LG Chem, Sanofi and AstraZeneca, has been a consultant for AstraZeneca, Novo Nordisk and Sanofi, has received speaker fees from Novo Nordisk, Boehringer Ingelheim, Handok, LG Chem, Novartis Korea, Hanmi, DongWha Pharm, Lilly Korea and AstraZeneca. The peer review history for this article is available at https://www.webofscience.com/api/gateway/wos/peer-review/10.1111/dom.15092. Data underlying the findings described in this manuscript may be obtained in accordance with AstraZeneca's data sharing policy described at https://astrazenecagrouptrials.pharmacm.com/ST/Submission/Disclosure. Data for studies directly listed on Vivli can be requested through Vivli at www.vivli.org. Data for studies not listed on Vivli could be requested through Vivli at https://vivli.org/members/enquiries-about-studies-not-listed-on-the-vivli-platform/. AstraZeneca Vivli member page is also available outlining further details: https://vivli.org/ourmember/astrazeneca/. Data S1.Supporting Information Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.
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.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".