Real-world data and evidence in pain research: an IMMPACT comprehensive review and best practice recommendations
Bibliographic record
Abstract
ABSTRACT: Real-world data (RWD) can be defined as routinely collected clinical or administrative data that might be used for research purposes and to generate real-world evidence (RWE). Computerized search and data mining methods, large electronic databases, and the development of novel computational and statistical methods allow for improved access to and analysis of RWD. Although RWD afford the opportunity to generate RWE with potentially improved efficiency and generalizability over prospective clinical studies, it is important to understand and apply best practices when analysing RWD, particularly when the goal is to generate RWE of diagnostic, prognostic, or treatment effectiveness. Real-world evidence can provide evidence complementary to randomized clinical trials (RCTs), especially in scenarios where RCTs are difficult to conduct. Real-world evidence studies need to be carefully designed, the research question clearly defined and addressable with the available RWD source, variables (treatment, outcome, covariates) operationalized, and hypotheses and analyses specified before data access. Sound interpretation of results requires a deep understanding of the benefits and limitations of RWE studies, including often deficient data quality, confounding, and other potential sources of bias. Registered protocols, registered reports as a publishing model, and/or restricted access to data until protocols are in place can be encouraged by journals and enforced by data guardians and will contribute to the emergence of high-quality RWD studies. Here, we summarize guidance documents on generating RWE of treatment effectiveness or comparative effectiveness, discuss the strengths and limitations of RWD and RWE, and provide recommendations for conducting effectiveness RWE studies in the pain field.
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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 | 0.085 | 0.238 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.024 | 0.017 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".