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
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it