Differences between the real and the desired worlds in the results of clinical trials
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.323 | 0.131 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it