Harmonizing Quality Improvement Metrics Across Global Trial Networks to Advance Paediatric Clinical Trials Delivery
Bibliographic record
Abstract
BACKGROUND: Despite global efforts to improve paediatric clinical trials, significant delays continue in paediatric drug approvals. Collaboration between research networks is needed to address these delays. This paper is a first step to promote interoperability between paediatric networks from different jurisdictions by comparing drivers for, and content of, metrics about clinical trial conduct. METHODS: Three paediatric networks, Institute for Advanced Clinical Trials for Children, the Maternal Infant Child and Youth Research Network and conect4children, have each developed metrics to address delays and create efficiencies. We identified the methodology by which each network identified metrics, described the metrics of each network, and mapped consistency to come to consensus about core metrics that networks could share. RESULTS: Metric selection was driven by site quality improvement in one network (11 metrics), by network performance in one network (13 metrics), and by both in one network (five metrics). The domains of metrics were research capacity/capability, site identification/feasibility, trial start-up, and recruitment/enrolment. The network driven by site quality improvement did not have indicators for capacity/capability or identification/feasibility. Fifteen metrics for trial start up and conduct were identified. Metrics related to site approvals were found in all three networks. The themes for metrics can inform the development of 'shared' metrics. CONCLUSION: We found disparity in drivers, methodology and metrics. Tackling this disparity will result in a unified approach to addressing delays in paediatric drug approvals. Collaborative work to define inter-operable metrics globally is outlined.
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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 | 0.451 | 0.510 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.013 | 0.016 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.019 | 0.019 |
| Open science | 0.006 | 0.017 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".