Advocating for drug development in newborn infants
Bibliographic record
Abstract
Neonatal care needs more robust guidance on pharmacotherapy, (formulation, dosage regimen, safety and efficacy information). This requires structured advocacy. We therefore discuss advocacy related to improving information about medicines including current practices, clinical trials, the current setting, and trial preparedness. This steps can improve neonatal drug development by generating evidence, particularly if a programmatic approach (identify dosing, eligibility criteria, and outcomes) to evidence generation is followed. Trial design should be guided by the intended use of the medicine and the benefits/risks that the study participant is exposed to. Regulatory trials (explanatory, controlled environment, internal validity, endpoints reflect clinically important outcomes, strong causal evidence) are sometimes necessary. However, some research questions are best addressed with informative trials. In either case, trial design can be supported by real world data and evidence, extrapolation from other subpopulations, or physiologically-based pharmacokinetic modeling. Data management, safety reporting, and management of drugs should be specified and proportionate. Trial design and conduct also necessitate awareness of Good Clinical Practice specific to neonates. Relevant aspects include protocol and trial design, research skills and interactions with Ethics Committees or Institutional Research Boards, capacities and competences needed within the research team, and aspects related to consent and recruitment.
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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 | 0.021 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".