Return of participant-level clinical trial results to participants: pilot of a simplified centralised approach
Bibliographic record
Abstract
OBJECTIVES: Public access databases such as clinicaltrials.gov achieve dissemination of clinical trial design and aggregated study results. However, return of participant-level data is rarely done. A key barrier includes the proprietary ownership of data by the sponsor. Additionally, investigators may not have access to centralised data, and per International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) Good Clinical Practice, must maintain the confidentiality of participants. This study piloted an approach to return both individual and aggregate clinical trial data to parents of children participating in a series of open-label clinical trials. SETTING AND DESIGN: A small biotech company obtained central ethics approval (centralised institutional review board [IRB], non-exempt). The study was advertised via parent advocacy groups. Parents of trial participants were offered the option to contact an employee (coordinator) within the company, requesting return of their child's study results. Ethics approval covered participation in six countries. The study focused on the sequential clinical trials of vamorolone VBP15-002 (NCT02760264) and VBP15-003 (NCT02760277) (post-results). INTERVENTIONS: Contact initiated by the parent enabled the coordinator to obtain informed consent (and separate General Data Protection Regulations consent), with phone translation when needed. Using date of birth and study site location provided by the parent, the data manager reported the participant number to the coordinator. The coordinator retrieved and compiled data, along with an aggregate summary, which was mailed via a password protected and encrypted memory device to the parent. Prereturn and postreturn surveys were sent to consented parents (n=19; 40% of 48 total trial participants) and investigators. RESULTS: Prereturn surveys indicated a request for as much data as offered, in all formats offered. Postreturn survey showed high satisfaction with the process and data returned. Survey of the physician site investigators (n=10; 100% participation of investigators) voiced general satisfaction with the process, with some reservations. CONCLUSIONS: This pilot study demonstrates an innovative, cost-effective, centralised and labour conservative approach to return of participant-level and aggregate data to participants in studies.
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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 | 0.304 | 0.304 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.007 | 0.011 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.027 | 0.006 |
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".