Estimation of patients potentially eligible for icosapent ethyl therapy in Italy through a review of literature data
Bibliographic record
Abstract
Introduction: The literature and international Guidelines agree in identifying LDL cholesterol (LDL-C) control asthe primary goal for reducing cardiovascular (CV) risk, while hypertriglyceridemia management is a secondaryobjective. Nevertheless, experimental evidence indicates that elevated triglyceride levels represent a significantCV risk factor.Epidemiological data about patients with hypertriglyceridemia and residual CV risk in Italy are limited. This studyaims to estimate the prevalence of these patients that, despite treatment with high-intensity statins plus ezetimibeand LDL-C levels < 70 mg/dL, may be eligible for treatment with icosapent ethyl (IPE) in Italy.Methods: A literature review was performed to identify evidence on the reimbursed indication of IPE in Italy.Data from literature were used to estimate the number of patients eligible for IPE treatment by calculating aminimum, a maximum, and an average scenario.Results: A total of 94 articles were identified, which, after screening, were reduced to 7 articles included in theanalysis. The analysis estimated a range of 2,500 to 78,590 total patients with residual hypertriglyceridemia potentially eligible for IPE treatment in Italy, with an average scenario of 22,427 patients.The number of eligible patients will depend on the prescription and reimbursement criteria established by AIFAand the evolution of clinical practice concerning lipid-lowering therapies.Conclusions: The reported evidence may be useful for physicians and healthcare policymakers in managing residual CV risk in populations already treated for hypercholesterolemia, contributing to more effective preventionof CV events.
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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.029 | 0.019 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".