Differences between the real and the desired worlds in the results of clinical trials
Bibliographic record
Abstract
OBJECTIVE: We refer to the effectiveness (known as pragmatic or real world) and efficacy (known as explanatory or desired or ideal world) of interventions. However, these terms seem to be randomly chosen by investigators who design clinical trials and do not always reflect the true purpose of the study. A pragmatic-explanatory continuum indicator summary tool was thus developed with the aim of identifying the characteristics of clinical trials that distinguish between effectiveness and efficacy issues. We verified whether clinical trials used the criteria proposed by the indicator summary tool, and we categorized these clinical trials according to a new classification. METHOD: A systematic survey of randomized clinical trials was performed. We added a score ranging from 0 (more efficacious) to 10 (more effective) to each domain of the indicator summary tool and proposed the following classifications: high efficacy (<25), moderate efficacy (25-50), moderate effectiveness (51-75), and high effectiveness (<75). RESULTS: A total of 844 randomized trials were analyzed. No analyzed trials used the criteria proposed by the indicator summary tool. Approximately 44% of the trials were classified as having moderate effectiveness, and 43.82% were classified as having moderate efficacy. CONCLUSIONS: Most clinical trials used the term "efficacy" to illustrate the application of results in clinical practice, but the majority of those were classified as having moderate effectiveness according to our proposed score. The classification based on the 0-100 score is still highly subjective and can be easily misunderstood in all domains based on each investigator's own experiences and knowledge.
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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 | 0.691 | 0.839 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.015 | 0.013 |
| Science and technology studies | 0.002 | 0.017 |
| Scholarly communication | 0.014 | 0.016 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".