Funding characteristics of randomised clinical trials supported by the Swiss National Science Foundation: a retrospective cohort study
Bibliographic record
Abstract
AIMS OF THE STUDY: Failure to publish publicly funded research represents a waste of scarce research resources across medical disciplines and countries. In Switzerland, about 40% of randomised clinical trials (RCTs) supported by the Swiss National Science Foundation (SNSF) were not published. We aimed to describe funding characteristics of published and unpublished RCTs supported by the SNSF, to quantify the amount of money spent for unpublished studies, and to compare our results to a similar study performed in the UK. METHODS: We established a retrospective cohort of RCTs funded by the SNSF up to 2015. For each RCT proposal, two investigators independently identified corresponding publications in electronic databases and trial registries. Teams of two investigators independently extracted details from the original SNSF proposal and, if available, from trial registries or publications. In addition, we surveyed principal investigators about trial costs and additional sources of funding. RESULTS: We included 101 RCTs supported by the SNSF between 1986 and 2015. Most were single-centre RCTs with a median of 138 participants (interquartile range [IQR] 76-400). Overall, 67 (67%) principal investigators responded to our main survey questions. Median total costs per RCT were CHF 428 000 (IQR 282 000-900 000) of which the SNSF provided a median CHF 222 000 (67% of total costs, IQR 40-80%). Most investigators (70%) mentioned additional funding, mainly from their own institution or private foundations. A total of CHF 6.7 million was granted to RCTs that remained unpublished. Funding characteristics were similar to publicly funded trials in the UK. CONCLUSIONS: A third of the total SNSF grant sum spent on healthcare RCTs between 1986 and 2015 did not result in peer-reviewed scientific publications. New SNSF grant schemes might improve publication outcomes but their effectiveness needs to be evaluated.
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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 | 0.696 | 0.684 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.034 | 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; 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".