IMPACT Observatory: tracking the evolution of clinical trial data sharing and research integrity
Bibliographic record
Abstract
INTRODUCTION: The opening of research data is emerging thanks to the increasing possibilities of digital technology. The opening of clinical trial (CT) data is a part of this process, expected to have positive scientific, ethical, health, and economic impacts thus contributing to research integrity. The January 2016 proposal by the International Council of Medical Journal Editors triggered ample discussion about CT data sharing and reconfirmed the need for an ongoing assessment of its dynamics. The IMProving Access to Clinical Trials data (IMPACT) Observatory aims to play such a role, and assess the data sharing culture, policies, and practices of key players, the impact of their interventions on CTs, and contribute to a transformation of research. The objective of this paper is to present the IMPACT Observatory as well as share some of its preliminary findings. MATERIALS AND METHODS: Methods include a scoping study of research, surveys, interviews, and an environmental scan of research data repositories. RESULTS: Our preliminary findings indicate that although opening of CT data has not yet been achieved, its evolution is encouraging. Initiatives by key players contribute to increasing of CT data sharing, and many barriers are shrinking or disappearing. CONCLUSIONS: The major barrier is the lack of data sharing standards, from preparing data for public sharing to its curatorship, findability and access. However, experiences accumulated by sharing CT data according to "upon request" or "open" mechanisms could inform the development of such standards. The Vivli, CORBEL-ECRIN and Open Trials projects are currently working in this direction.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | MetaresearchResearch integrity Domain: Reproducibility · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | MetaresearchOpen scienceResearch integrity Domain: Reproducibility · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.479 | 0.639 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.023 | 0.030 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.019 | 0.022 |
| Open science | 0.005 | 0.028 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".