Prospective Trial Registration and Publication Rates of Randomized Clinical Trials in Digital Health: A Cross Sectional Analysis of Global Trial Registries
Bibliographic record
Abstract
Abstract Registration of clinical trials was introduced to mitigate the risk of publication and selective reporting bias in the realm of clinical research. The prevalence of publication and selective reporting bias in trial results has been evidenced through scientific research. This bias may compromise the ethical and methodological conduct in the design, implementation and dissemination of evidence-based healthcare interventions. Principal investigators of digital health trials may be overwhelmed with challenges that are unique to digital health research, such as the usability of the intervention under test, participant recruitment, and retention challenges that may contribute to non-publication rate and prospective trial registration. Our primary research objective was to examine the prevalence of prospective registration and publication rates in digital health trials. We included 417 trials that enrolled participants in 2012 and were registered in any of the seventeen WHO registries. The prospective registration and publication rates were at (38.4%) and (65.5%) respectively. We identified a statistically significant ( P <.001) “Selective Registration Bias” with 95.7% of trials published within a year after registration, were registered retrospectively. We reported a statistically significant relationship ( P =.003) between prospective registration and funding sources, with industry-funded trials having the lowest compliance with prospective registration at (14.3%). The lowest non-publication rates were in the Middle East (26.7%) and Europe (28%), and the highest were in Asia (56.5%) and the U.S. (42.5%). We found statistically significant differences ( P <.001) between trial location and funding sources with the highest percentage of industry funded trials in Asia (17.3%) and the U.S. (3.3%).
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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 | 0.384 | 0.617 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.007 |
| Bibliometrics | 0.011 | 0.016 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".