OHL sufre un castigo injusto: La compañía no merece el grado de bono basura
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Bibliographic record
Abstract
In patients with type 2 diabetes mellitus (T2DM) and cardiovascular (CV) disease, empagliflozin (EMPA) decreased progression of chronic kidney disease (CKD), likely via a reduction in intraglomerular pressure. Due to prevalent comorbidities, such as hypertension and albuminuria, patients often receive other agents that alter intrarenal hemodynamics, including angiotensin converting enzyme inhibitors/angiotensin receptor blockers (ACEi/ARBs), calcium channel blockers (CCBs) and diuretics. Nonsteroidal anti-inflammatory drugs (NSAIDs) may also be used by some individuals. In this exploratory, non-prespecified analysis, we investigated whether the kidney benefits of EMPA are altered in individuals already using the medications in these categories. In the BI 10773 (Empagliflozin) Cardiovascular Outcome Event Trial in Type 2 Diabetes Mellitus Patients (EMPA-REG OUTCOME<sup>®</sup>) trial, 7020 patients were essentially equally randomized to EMPA 10 mg, 25 mg or placebo added to their standard care. Differences in risk of incident or worsening nephropathy for pooled EMPA vs placebo across subgroups by baseline background medications (to which patients were not randomized) were assessed using a Cox proportional hazards model. Risk reductions in incident or worsening nephropathy with EMPA were consistent across medication subgroups, with no heterogeneity of treatment effect. As a representative example, the risk for acute renal failure was overall slightly increased in patients using ACEi/ARBs in all groups (placebo, EMPA 10 mg or EMPA 25 mg) but incidence rates were numerically lower in those assigned to EMPA. Similar patterns were observed for other medications included in this analysis. Thus, EMPA may assist to prevent CKD progression in patients with T2DM with CV disease, irrespective of common background medications that alter intrarenal hemodynamics, and without increasing acute renal adverse events.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.006 |
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 it