Investigators' sense of failure thwarted transparency in clinical trials discontinued for poor recruitment
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: When a randomized clinical trial (RCT) prematurely discontinues, it is essential that stakeholders do the right thing to ensure that lessons can be learnt and trust in clinical research is maintained. There is, however, a lack of evidence exploring this issue. This study aimed to examine clinical trial stakeholders' practices following trial discontinuation due to poor participant recruitment and their views on implications of such discontinuation. METHODS: Individual semi-structured qualitative interviews were conducted with 49 clinical trial stakeholders from Switzerland (n = 39), Germany (n = 9) and Canada (n = 1) between August 2015 and November 2016. RESULTS: After interviews with 49 clinical trial stakeholders (75% male presenting), it was found that stakeholders were aware of the risks of premature trial discontinuation wasting limited resources, adversely impacting scientific evidence, and having negative personal and professional implications. However, barriers continue to undermine transparency regarding trial discontinuation in practice, with it being reported that most investigators of discontinued trials are failing to notify stakeholders or publishing their results. Investigators sense of failure and associated negative emotions were identified as a key reason why investigators are not more transparent following discontinuation. CONCLUSION: The decision to notify stakeholders and publish results of a discontinued clinical trial should not rest solely on individual investigators but come from a systemic approach. However, until health research proactively requires the dissemination of results of all clinical trials, much will rest on individual investigators being motivated to do the right thing. Support programs might be helpful for investigators involved in discontinued trials and promote transparency and learning lessons.
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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 | 0.404 | 0.586 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.014 | 0.029 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.009 | 0.014 |
| 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".