Empagliflozin promises to bridge the gap between non-alcoholic fatty liver disease, type 2 diabetes, and cardiovascular disease
Bibliographic record
Abstract
ENWEndNote BIBJabRef, Mendeley RISPapers, Reference Manager, RefWorks, Zotero AMA Patoulias D, Kalogirou M. Empagliflozin promises to bridge the gap between non-alcoholic fatty liver disease, type 2 diabetes, and cardiovascular disease. Gastroenterology Review/Przegląd Gastroenterologiczny. 2018;13(4):337-339. doi:10.5114/pg.2018.79815. APA Patoulias, D., & Kalogirou, M. (2018). Empagliflozin promises to bridge the gap between non-alcoholic fatty liver disease, type 2 diabetes, and cardiovascular disease. Gastroenterology Review/Przegląd Gastroenterologiczny, 13(4), 337-339. https://doi.org/10.5114/pg.2018.79815 Chicago Patoulias, Dimitrios, and Maria Kalogirou. 2018. "Empagliflozin promises to bridge the gap between non-alcoholic fatty liver disease, type 2 diabetes, and cardiovascular disease". Gastroenterology Review/Przegląd Gastroenterologiczny 13 (4): 337-339. doi:10.5114/pg.2018.79815. Harvard Patoulias, D., and Kalogirou, M. (2018). Empagliflozin promises to bridge the gap between non-alcoholic fatty liver disease, type 2 diabetes, and cardiovascular disease. Gastroenterology Review/Przegląd Gastroenterologiczny, 13(4), pp.337-339. https://doi.org/10.5114/pg.2018.79815 MLA Patoulias, Dimitrios et al. "Empagliflozin promises to bridge the gap between non-alcoholic fatty liver disease, type 2 diabetes, and cardiovascular disease." Gastroenterology Review/Przegląd Gastroenterologiczny, vol. 13, no. 4, 2018, pp. 337-339. doi:10.5114/pg.2018.79815. Vancouver Patoulias D, Kalogirou M. Empagliflozin promises to bridge the gap between non-alcoholic fatty liver disease, type 2 diabetes, and cardiovascular disease. Gastroenterology Review/Przegląd Gastroenterologiczny. 2018;13(4):337-339. doi:10.5114/pg.2018.79815.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.000 |
| 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".