29 Five-year report on the activities of the Connect4Children (c4c) European paediatric clinical trials network in Belgium
Bibliographic record
Abstract
Background/ Aims Currently, over 60% of clinical trials in children are unsuccessful due to insufficient recruitment, administrative burden, inadequate methodology, among other reasons. Sponsors experience difficulties identifying adequate and developed sites to conduct pediatric trials. To optimize and facilitate clinical trials, (inter)national networks have been developed by (academic) investigators such as the I-ACT for Children network (U.S.A.), Innovative Medicines Initiative 2 conect4children (IMI-c4c) grouped network (Europe), and MICRYN (Canada). For Belgium, the Paediatric Clinical Trial Network within BPCRN Belgian Paediatric Clinical Research Network), was developed in 2009 and included in c4c in 2018, managed by the Ghent University (Hospital). Method This report describes an update on the progress of c4c over the past 5 years. The Belgian Paediatric Clinical Trial Network has 15 hospitals connected. The network has been involved in 2 academic trials, 10 industry trials and over 35 preliminary feasibility requests. Results The Belgium national network (BPCRN) was selected in 2/4 academic trials, and 5/5 industry trials within c4c. Constructive communication with the sponsor increased the number of sites from 2 to 8 for the academic studies. The added value of the BPCRN in the academic trials was help by the national hub in regulatory submission and budget plan. For the industry studies, the administrative requests have been centrally buffered, with over 66% of the feasibility questionnaires being pre-filled. Moreover, an additional 32% new sites were identified for the industry sponsors. The network is also involved in i) data standardization, inclusion of real-world data and rare disease data-dictionary development, ii) expert panels and iii) teaching pan-European courses for site and investigator development. Moreover, the Belgian network has been the liaison for the totality of European national networks with the US-based network I-ACT for Children, for clinical trials running outside of the c4c project scope.Conclusion Over the past 5 years, substantial developments and progress have been made with the Belgium network activity by BPCRN. Central optimalisation of site identification, site development and trial start-up have been prioritized. To achieve a sustainable network after IMI2 funding, a longitudinal commitment of both sites and sponsors is needed.
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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 | 0.076 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.026 | 0.015 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".