Teaching Real-World Evidence: Protocol for a Systematic Review
Bibliographic record
Abstract
BACKGROUND: Real-world evidence (RWE) refers to observational health care data beyond clinical trial data. It holds the promise of transforming health care as a new form of evidence to support decision makers in making decisions when developing and regulating medicines. As the importance of RWE is recognized by industry and regulatory bodies, teaching RWE becomes an important matter to evaluate and refine in order to develop future researchers and stakeholders who can better integrate RWE into the routine development of medicine. OBJECTIVE: The aim of this review is to understand how RWE is currently being taught. From this landscape study, the insufficiencies of the current education of RWE can be identified and subsequently inform future education policies around RWE and its subfacets. METHODS: We will search MEDLINE, EMBASE, PsycINFO, Healthcare Management Information Consortium, Cochrane, and Web of Science for published studies using a combination of keywords and subject headings related to RWE and education. In addition, a Google search to identify grey literature will be conducted. Two authors will independently screen the titles and abstracts identified from the search and accept or reject the studies according to the study inclusion criteria; any discrepancies will be discussed and resolved. The quality of the included literature will be assessed using the Critical Appraisal Skills Programme systematic review checklist. RESULTS: Data from eligible publications will be abstracted into a predesigned form in order to better understand the current state of education of RWE and inform future RWE education directions and policies. CONCLUSIONS: The subsequent systematic review will be published in a peer-reviewed journal. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/16941.
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 | 0.121 | 0.145 |
| Meta-epidemiology (narrow) | 0.008 | 0.008 |
| Meta-epidemiology (broad) | 0.020 | 0.018 |
| Bibliometrics | 0.015 | 0.017 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.134 | 0.027 |
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".