Il ruolo della Real World Evidence nella fase pre-marketing dei farmaci: le esperienze e le prospettive future di Fondazione ReS nel delineare i confini delle Target Population
Bibliographic record
Abstract
The role of Real-World Evidence (RWE) concerns the entire drug lifecycle; despite widely recognised in the post-marketing, it is still debated in the pre-marketing, mainly as per the identification and analysis of target populations (TPs) for new drugs or indications. Through administrative healthcare databases, Research and Health Foundation (ReS), in collaboration with experts, develops algorithms to select and analyse TPs. As of March 2024, 85 TPs in 15 clinical areas have been analysed, of which oncology is the most represented. Findings on prevalence and incidence of specific diseases (or subpopulations), patient characteristics and costs directly charged to the Italian National Health Service, are provided. These are useful for healthcare institutions and pharmaceutical companies. In the future, efforts will focus on the development of tools based on artificial intelligence and synthetic data to improve analyses of TPs and support regulatory decisions 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 | 0.177 | 0.360 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.016 | 0.014 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".