Real-world data: bridging the gap between clinical trials and practice
Bibliographic record
Abstract
Real-world data (RWD) are rapidly emerging sources of information for patients, clinicians and regulators. While randomized controlled trials (RCTs) reduce bias and confounding through the randomization process and provide the highest quality of evidence regarding drug efficacy, RCTs may be impractical or unfeasible for rare diseases or disease subsets. And yet, studies attempting to replicate clinical trial results using observational datasets have failed. Given the inherent differences between observational data and clinical trial results, this discordance is not surprising. However, RWD may still have independent value as complementary tools to trial results. In this viewpoint, we explore the challenges of RWD and discuss key questions that clinicians, patients, and regulators will need to consider when faced with positive efficacy data from clinical trials, and negative effectiveness data from real world studies. Finally, we explore novel trial designs that might help bridge the gap from RCTs to RWD.
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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 | 0.491 | 0.368 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.014 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 0.006 |
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; both teacher heads 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".