How to generalize efficacy results of randomized trials: recommendations based on a systematic review of possible approaches
Bibliographic record
Abstract
RATIONALE, AIMS AND OBJECTIVES: Randomized controlled trials (RCTs) are the preferred source for evidence for the effect of treatment. However, patients participating in RCTs often manifest important differences from patients seen in practice. Therefore, guideline developers have to decide whether the results are generalizable to the target population not represented in RCTs. METHOD: A systematic review of the literature was undertaken to identify methods to decide whether to generalize the results from RCTs to patients who were not represented in these trials. RESULTS: One approach is to examine the in- and exclusion criteria of trials and infer from these whether the trial population was sufficiently representative. Other authors suggest, because of the inclusion of a broader range of patients, reliance on observational studies if no direct evidence for the target population is available. Another approach is to apply the relative effect of treatment found in trials to patients in practice unless there is a compelling reason to believe the results would differ substantially as a function of particular characteristics of those patients. Although there are exceptions, this approach is supported by empirical evidence that, in general, relative effect of treatment on benefit outcomes seldom differs to an important extent across subgroups of patients. CONCLUSION: We propose this last approach: focusing on RCTs unless there is a compelling reason not to do so. Compelling reasons will most often be found with respect to issues of rare adverse effects, for which observational studies are likely to provide the best estimates.
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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 | 0.420 | 0.721 |
| Meta-epidemiology (narrow) | 0.008 | 0.008 |
| Meta-epidemiology (broad) | 0.022 | 0.027 |
| Bibliometrics | 0.028 | 0.017 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.012 | 0.019 |
| Open science | 0.019 | 0.008 |
| Research integrity | 0.029 | 0.023 |
| Insufficient payload (model declined to judge) | 0.007 | 0.007 |
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; the direct Gemma label and the distilled Codex classifier 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".