Interpretation and Impact of Real-World Clinical Data for the Practicing Clinician
Bibliographic record
Abstract
Real-world studies have become increasingly important in providing evidence of treatment effectiveness in clinical practice. While randomized clinical trials (RCTs) are the "gold standard" for evaluating the safety and efficacy of new therapeutic agents, necessarily strict inclusion and exclusion criteria mean that trial populations are often not representative of the patient populations encountered in clinical practice. Real-world studies may use information from electronic health and claims databases, which provide large datasets from diverse patient populations, and/or may be observational, collecting prospective or retrospective data over a long period of time. They can therefore provide information on the long-term safety, particularly pertaining to rare events, and effectiveness of drugs in large heterogeneous populations, as well as information on utilization patterns and health and economic outcomes. This review focuses on how evidence from real-world studies can be utilized to complement data from RCTs to gain a more complete picture of the advantages and disadvantages of medications as they are used in practice.Funding: Sanofi US, Inc.
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 | 0.188 | 0.496 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.003 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".