Protocol of the Long-term Impact of RAS Inhibition on Cardiorenal Outcomes (LIRICO) randomized trial
Bibliographic record
Abstract
Microalbuminuria is a strong, consistent and independent risk factor for cardiovascular and renal disease in patients with diabetes and/or hypertension and in the general population. Several randomized trials have shown the efficacy of inhibiting the renin-angiotensin system (RAS) with angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin II receptor blockers (ARBs) to prevent cardiovascular events and the progression of kidney disease. These 2 classes of drugs are equally effective for renal outcomes in patients with diabetic nephropathy, but only ACEIs have been found to significantly impact the risk of all-cause mortality, predominantly cardiovascular, in patients with diabetic nephropathy. Studies on the cardiorenal efficacy of combined therapy with ACEIs and ARBs in individuals with microalbuminuria or macroalbuminuria and other cardiovascular risk factors have been inconclusive. The Long-term Impact of RAS Inhibition on Cardiorenal Outcomes (LIRICO) study aims to address existing questions in this setting. This is a phase III, randomized, comparative, pragmatic trial with prospective randomized open blinded endpoint (PROBE) design. It will evaluate the comparative efficacy of combined therapy with ACEIs and ARBs versus monotherapy with either ACEIs or ARBs in improving cardiovascular and renal outcomes in microalbuminuric or macroalbuminuric individuals at cardiorenal risk. The study will enroll 2,100 patients, selected in a network of internal medicine, diabetology or nephrology outpatient clinics. Patients will be randomly allocated to ACEIs, ARBs or their combination. The study has been approved and funded by the Agenzia Italiana del Farmaco (A.I.F.A.) within the 2005 funding plan for independent research on drugs.
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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 | 0.021 | 0.029 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.075 | 0.016 |
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".