Moderated ePoster Session 2
Bibliographic record
Abstract
Heart failure (HF) and type 2 diabetes mellitus (T2DM) are both growing public health concerns contributing to major medical and economic burdens to society. T2DM increases the risk of HF, frequently occurs concomitantly with HF, and worsens the prognosis of HF. Several anti-hyperglycaemic medications have been associated with a concern for worse HF outcomes. More recently, the results of the EMPA-REG OUTCOME trial showed that the sodium-glucose co-transporter 2 (SGLT2) inhibitor empagliflozin was associated with a pronounced and precocious 38% reduction in cardiovascular mortality in subjects with T2DM and established cardiovascular disease [Correction added on 8 September 2017, after first online publication: "32%" in the previous sentence was corrected to "38%"]. These benefits were more related to a reduction in incident HF events rather than to ischaemic vascular endpoints. Several mechanisms have been put forward to explain these benefits, which also raise the possibility of using these drugs as therapies not only in the prevention of HF, but also for the treatment of patients with established HF regardless of the presence or absence of diabetes. Several large trials are currently exploring this postulate.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.548 | 0.228 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".