Expert Perspectives on Next Generation Health Guidelines: How to Integrate RWE in EBM
Bibliographic record
Abstract
Integrating real-world evidence (RWE) into evidence-based medicine (EBM) enhances healthcare decision-making. RWE provides insights into the real-world effectiveness and safety of therapies and health technologies, filling gaps that clinical trials may leave. EBM, which concentrates on therapeutic issues, depends on rigorous evaluation of evidence, including data from randomized controlled trials (RCTs) and RWE. Combining evidence from RCTs and RWE when forming recommendations offers a comprehensive understanding of benefits and risks by considering their strengths, limitations, and standardized methods. The 2nd European Academy of Allergy & Clinical Immunology/Respiratory Effectiveness Group (EAACI/REG) Workshop, held in Rome, Italy, on October 4th, 2023, discussed integrating RWE and EBM. The goals were to develop recommendations for high-quality RWE and its inclusion in evidence syntheses, with a particular focus on airway diseases. During the discussion, key topics emerged. An "action plan" is needed to share these topics in various formats. RCTs are currently seen as providing the strongest evidence, so how to incorporate Non-Randomized Studies of Interventions (NRSI) requires careful consideration. An educational plan and collaboration with patients' organizations are also very important. A collaborative approach involving patients, clinicians, and regulators is essential for achieving meaningful results and can be adapted as needed for cultural differences. A "glossary" of terms used in this context will be created to improve understanding. Setting benchmarks for data quality and reliability, such as quality thresholds, in disease-specific studies requires collaboration with research method experts. Managing and recording registries according to standardized protocols and quality standards from well-designed registries will ensure the data is valid and accurate.
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 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".