Exploring scalable assessment methods for terminated trials in ClinicalTrials.gov: A cohort analysis of German and Californian trials
Bibliographic record
Abstract
INTRODUCTION: Clinical trials can terminate early for many reasons, including non-scientific reasons. We aimed to develop scalable semi-automated methods to characterize terminated trials and explore a methodology to estimate the risk of experiencing serious adverse events (SAEs) in trials terminated due to non-scientific reasons. METHODS: Two cohorts of clinical trials registered in ClinicalTrials.gov were investigated: (1) a cohort of clinical trials affiliated with German university medical centers (reported as completed between 2009-2017) and (2) a cohort of clinical trials affiliated with Californian university medical centers (reported as completed between 2014-2017). We used these cohorts to explore scalable assessment methods and compare terminated trials to completed ones regarding trial characteristics, including therapeutic focus. In a subset of trials terminated for non-scientific reasons with tabular summary results and a parallel, randomized design, we estimated additional risk for SAE. For the German cohort, if results were missing from ClinicalTrials.gov, results from the EU Clinical Trials Register (EUCTR) were included if available. RESULTS: Of 2,253 German and 1,091 Californian trials, 217 (10%) and 150 (14%) were terminated, respectively. The majority (German: 65%, Californian: 67%) cited non-scientific reasons for termination, primarily low accrual. Compared to completed trials, terminated trials showed lower rates of results reporting: 35% vs. 78% in Germany and 72% vs. 87% in California. Of 242 trials terminated for non-scientific reasons, 14 (6%; 11 from ClinicalTrials.gov, 3 from EUCTR) could be included in the SAE risk assessment. In this limited exploratory analysis, no difference in SAE risk was observed between the intervention and control arms (RR 1.05, 95% CI: 0.76-1.44). DISCUSSION: Results of terminated trials were less frequently reported, limiting opportunities for knowledge generation. Broader adoption of harmonized result reporting standards across registries, structured templates, and improved logic checks could enable scalable assessment approaches and enhance the utility of terminated trial data for clinical research transparency.
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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 | 0.461 | 0.659 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.009 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".