An evidence‐based systematic review of the off‐label uses of lisinopril
Bibliographic record
Abstract
AIMS: Lisinopril is an angiotensin-converting-enzyme inhibitor that is largely administered for off-label uses. This study aims to provide a comprehensive review of off-label uses of lisinopril to aid physicians to make evidence-based decisions. METHODS: The following bibliographic databases were searched from inception up to 30 March 2017: PubMed, EMBASE, the Cochrane Library, Cochrane Central Register of Controlled Trials, Scopus, Ovid and Proquest. This systematic review sought all randomized trials conducted on adult individuals comparing lisinopril on its off-label uses with alternative drugs or placebos and reported direct or alternative clinical outcomes. Risk of bias assessment by using the Cochrane Collaboration risk-of-bias tool and quality evaluation took place. RESULTS: . Lisinopril offered better outcomes in comparison to other standard treatments of diabetic nephropathy. Other studies showed positive effects of lisinopril for migraine, prevention of diabetes, myocardial fibrosis, mitral valve regurgitation, cardiomyopathy in patients with Duchenne muscular dystrophy, oligospermia and infertility, and diabetic retinopathy. Conversely, the studies reported that lisinopril was ineffective for five other off-label uses. CONCLUSIONS: The identified studies showed that lisinopril was highly effective for proteinuric kidney disease with a minor but inconsiderable decrease in GFR. Positive effects of lisinopril were demonstrated in seven other off-label uses; however, lisinopril cannot be recommended as the first choice for these until further clinical trials confirm these positive effects.
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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 | 0.016 | 0.060 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".