A PAN-CANADIAN STUDY ON THE COMPOUNDED MEDICINES MOST IN NEED OF COMMERCIALIZED ORAL PEDIATRIC FORMULATIONS
Bibliographic record
Abstract
Abstract BACKGROUND A large number of drugs administered to children have no commercially available formulations. As a result, health care providers and parents manipulate dosage forms designed for adults. Although compounding is essential to increase access to medicines for children, it can result in adverse events or therapeutic failure. There is an urgent need to undertake a mapping of the needs for child-friendly medicines in Canada. OBJECTIVES To determine: 1) the most frequently compounded medicines in Canadian paediatric hospitals; 2) the challenges associated with drug compounding; and 3) medicines most in need of commercialized oral paediatric formulations. DESIGN/METHODS Sixteen Canadian paediatric academic hospitals were contacted to participate in a telephone survey including 12 open-, close-ended or Likert-scale questions. RESULTS Thirteen centers participated in the survey (81.3%). Fifty-three drugs were identified as most in need of a commercialized oral paediatric formulation. Of those, 12 were reported by ≥4 hospitals as a priority (Table). The most frequently reported compounding challenges were: lack of standardization, bad taste, lack of awareness of prescribers, stability of the formulation, and availability of compounding pharmacies. CONCLUSION This study highlights which drugs are most needed for paediatric oral formulations in Canada. For compounded medicines with paediatric formulations available in other countries we are currently assessing their adequacy and partnering with pharmaceutical industry to bring them to the Canadian market. As for those medicines without paediatric formulations in Canada or abroad we are looking for partners interested in developing such formulations. Furthermore, harmonized regulations and data-sharing should be pursued to facilitate access to child-friendly medicines.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".