Evidence of acceptability of oral paediatric medicines: a review
Bibliographic record
Abstract
OBJECTIVES: The aim of this review was to map the currently available evidence on acceptability of oral paediatric medicines to aid in the selection of suitable platform formulations for the development of new acceptable paediatric products. METHODS: This process used a defined search strategy of indexed publications and included methods to assess the quality of the evidence retrieved. KEY FINDINGS: Taste/palatability was the most extensively studied area of paediatric medicine acceptability yet standard methods or criteria that define what is classed as acceptable to children is still to be defined. There have been many reports on the acceptability of medicines to paediatric populations yet major gaps in the acceptability knowledge base exist including the shape and dimensions of tablets, minitablets and capsules swallowed whole in infants and children; size and overall volume of multiparticulates; volume of liquids completely swallowed in infants and children; duration of retention within the oral cavity, size and taste of orodispersible tablets, lozenges and chewable tablets and the number of solid units dosed at each time point. CONCLUSIONS: The review highlights where further information is required to support knowledge around acceptability of age-appropriate medicines. An algorithm to aid in selection of a formulation that is likely to be acceptable based on the age range to be treated by the medicine is presented as a result of this review.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".