PP-24 Consent in neonatal research: a delphi survey interrogating parents of preterm newborns and healthcare professionals
Bibliographic record
Abstract
Background Adherence and obtaining parental con-sent for their children’s participation are key factors in clinical neonatal research influenced by: the quality of the information delivered and the interaction between parents and investigators. This exchange of information is one of the most serious challenges as failure to disclose some important items by clinicians would lead to difficul-ties in decision making by parents, not being an informed consent process anymore. This Delphi survey aims to es-tablish a consensus between parents of preterm infants and healthcare professionals on the information criteria deemed essential by both parties in order to improve the recruitment of newborns in clinical trials. Methods This study has been conducted among par-ents of preterm newborns and healthcare professionals (pediatri Drug use during pregnancy: a systematic re-viewcians, neonatologists, obstetricians; experts in phar-macology, researchers, clinical research studies coordina-tors; ethicists and members of ethics committees). In this 3-phase study, the items were defined by the Scientific Committee (CS), composed of 11 clinicians (from 7 coun-tries: Belgium, Canada, France, Spain, Switzerland, United Kingdom, United States) and 1 European representative of newborn parents associations (European Foundation for the Care of Newborn Infants, EFCNI). Then, the Com-mittee of Experts (CE), composed of 16 clinicians and 16 parents, members of preterm newborns parents associa-tions (with a balanced distribution in 10 countries), evalu-ates these items on two occasions. Results 96 items were selected by the SC, submitted and evaluated by the CE on a scale from 1 to 9 accord-ing to the importance they had for them, based on their personal experience and beliefs. In the first round, 63/96 items were retained (first level of consensus criteria: me-dian score above 7, 65% of responses in the top tertile between 7 and 9-). The second round, in progress, will refine this selection and only items meeting the second level of consensus criteria (median score above 7, 75% of responses in the top tertile -between 7 and 9-) will be conserved for the final consensus. Conclusion This parental/professional consensus will improve parents’ information and decision-making pro-cess, respecting ethical and quality criteria to include newborns in clinical trials.
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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 | 0.087 | 0.095 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".