Optimizing the Use of Electronic Data Sources in Clinical Trials: The Technology Landscape
Bibliographic record
Abstract
TransCelerate has created an initiative to facilitate the industry's movement toward optimal use of electronic data sources for clinical research. Although guidance and standards have been in place for some time, gaps remain. Consequently, transcription among electronic systems continues to be the norm. In the initial phase of the eSource Initiative, TransCelerate is developing a thorough understanding of the current landscape. As a preliminary step in this process, the TransCelerate eSource Initiative published Optimizing the Use of Electronic Data Sources in Clinical Trials: The Landscape Part I, which provided insight into sponsor company eSource activities and the environment affecting eSource adoption based on input from TransCelerate member companies, standards organizations, and regulatory authorities. For Part II (this article), TransCelerate surveyed technology companies, including CROs providing technology, to better understand capabilities available today, plans for eSource, and perceived barriers to greater adoption. This information is a vital input that will help shape upcoming TransCelerate proposals for best practices for industry utilization of electronic data collection tools and methods. It is clear from the survey results that the technologies needed to support the various eSource modalities are mature. However, the approach to implementing eSource is fragmented. Greater collaboration is needed not only within the pharmaceutical industry but across industries that include health care and technology. The industry must reach common understandings about novel endpoints, data standards, system validation, and related issues. While technology in itself is not a significant barrier to eSource implementation, interoperability among systems is an enormous challenge to establishing a complete end-to-end electronic health care and research ecosystem. The TransCelerate eSource Initiative will continue to evaluate the technology, regulatory environment, data standards, and health care landscape to support the goal of improving global clinical science and global clinical trial execution. Forthcoming publications will focus on future vision and demonstration projects.
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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 | 0.494 | 0.499 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.030 | 0.037 |
| Open science | 0.006 | 0.015 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".