Designing for Dyads: A Multidisciplinary Panel on Inclusion of Pregnant and Lactating Persons and Their Infants in Clinical Trials
Bibliographic record
Abstract
There is international momentum to improve the representation of pregnant and lactating persons and neonates in clinical trials to generate equitable and robust data for these groups. Appropriate inclusion of these groups in clinical trials requires additional considerations owing to alterations in pharmacokinetics of medicines during pregnancy, evaluating newborn outcomes and exposures through lactation, ethical issues relating to the timing of and approach to informed consent, and a lack of regulatory incentives or frameworks to guide trial design. These factors, combined with low overall knowledge of clinical trials, make it challenging to engage health care providers and patients in discussions about clinical trials during pregnancy. A multidisciplinary approach is needed to develop guidance for researchers, clinicians, industry, and regulatory agencies to promote safe participation. We herein provide a summary of the discussion from a multidisciplinary panel entitled "Designing for Dyads" that was held at the 2024 Increasing capacity for Maternal and Paediatric Clinical Trials summit in Vancouver, BC, Canada and the action items suggested by the panel.
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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 | 0.442 | 0.379 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.036 | 0.012 |
| Scholarly communication | 0.015 | 0.014 |
| Open science | 0.008 | 0.047 |
| Research integrity | 0.025 | 0.042 |
| Insufficient payload (model declined to judge) | 0.009 | 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".