Leading Paediatric Infectious Diseases—Current Trends, Gaps, and Future Prospects in Oral Pharmacotherapeutic Interventions
Bibliographic record
Abstract
Paediatric infectious diseases contribute significantly to global health challenges. Conventional therapeutic interventions are not always suitable for children, as they are regularly accompanied with long-standing disadvantages that negatively impact efficacy, thus necessitating the need for effective and child-friendly pharmacotherapeutic interventions. Recent advancements in drug delivery technologies, particularly oral formulations, have shown tremendous progress in enhancing the effectiveness of paediatric medicines. Generally, these delivery methods target, and address challenges associated with palatability, dosing accuracy, stability, bioavailability, patient compliance, and caregiver convenience, which are important factors that can influence successful treatment outcomes in children. Some of the emerging trends include moving away from creating liquid delivery systems to developing oral solid formulations, with the most explored being orodispersible tablets, multiparticulate dosage forms using film-coating technologies, and chewable drug products. Other ongoing innovations include gastro-retentive, 3D-printed, nipple-shield, milk-based, and nanoparticulate (e.g., lipid-, polymeric-based templates) drug delivery systems, possessing the potential to improve therapeutic effectiveness, age appropriateness, pharmacokinetics, and safety profiles as they relate to the paediatric population. This manuscript therefore highlights the evolving landscape of oral pharmacotherapeutic interventions for leading paediatric infectious diseases, crediting the role of innovative drug delivery technologies. By focusing on the current trends, pointing out gaps, and identifying future possibilities, this review aims to contribute towards ongoing efforts directed at improving paediatric health outcomes associated with the management of these infectious ailments through accessible and efficacious drug treatments.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".