A blueprint for success in real-world evidence: “glocal” approach to building capabilities and generating impactful evidence
Bibliographic record
Abstract
The past decade has seen the increasing influence and relevance of real-world data (RWD) and real-world evidence (RWE) in healthcare decision making. The value added by RWD/RWE has prompted the pharmaceutical industry to develop high performing systems and practices to harness the power of evidence generated at the global level. However, this worldwide transformation provides outstanding opportunities to support capability building within local affiliates and to impact key country-level stakeholders through resulting evidence. Therefore, we present an Evidence Blueprint Initiative, which links the global and local ("glocal") skills, and furthermore addresses the opportunities and gaps in evidence generation capabilities at the local level. Cross-functional experts were recruited at the local, regional, and global level to define best practices. A framework was developed to characterize the foundational expertise needed and to assess markets' existing capabilities. Subsequently, targeted roadmaps were developed and implemented to build capabilities in specific areas within each affiliate. The impact from the Blueprint is encouraging, resulting in improved local evidence plans, established evidence teams, enhanced RWD use and strategic implementation of patient centric science in local affiliates. The success of the Blueprint resides in empowering affiliates to realise their local evidence generation ambitions and to match them to their local context. It strengthens and expands the ties between various parts of the organisation and the external environment while building fit-for-future evidence capabilities from local affiliates.
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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 | 0.385 | 0.353 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.021 | 0.009 |
| Science and technology studies | 0.011 | 0.101 |
| Scholarly communication | 0.047 | 0.047 |
| Open science | 0.010 | 0.058 |
| Research integrity | 0.017 | 0.034 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".