Empirical investigation of resource use and costs of investigator-initiated randomized controlled trials in four countriesled
Bibliographic record
Abstract
Randomized Controlled Trials (RCTs) are considered the gold standard to evaluate the safety and efficacy of treatments and interventions in clinical research (1). Conducting high-quality RCTs is challenging, time consuming and resource intensive (2–4). Consequently, tens of billions of dollars of public and private money are invested into this sector every year (5). Furthermore, RCT costs are rising in response to escalating trial complexity, stricter regulation, and increasing administrative burden (2,6–9). Academic investigators usually depend on scarce financial resources, thus efforts to improve the cost-effectiveness of RCTs are urgently needed. Current literature, however, lacks systematically collected empirical data on detailed resource use and costs of investigator-initiated RCTs. The aim of this study is to generate a database of detailed empirical resource use and cost data from 180 investigator-initiated RCTs in Switzerland, Germany, Canada, and the United Kingdom (UK).
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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 | 0.571 | 0.850 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.031 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.536 | 0.025 |
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".