Paediatric clinical research from the perspective of hospital pharmacists from France and Canada
Bibliographic record
Abstract
OBJECTIVES: To compare pharmacy support for paediatric research services in France and Canada and to describe the perception of pharmacists and rank the paediatric clinical research issues. METHODS: This was a cross-sectional descriptive study. All paediatric hospitals from Canada and the main hospitals from France were contacted. A survey was conducted from May-September 2012. Descriptive statistics were performed. KEY FINDINGS: Results from 11 paediatric hospitals in Canada (11/12, 92%) and 11 (11/18, 61%) in France were obtained. There was a similar number of ongoing paediatric clinical trials per hospital in France versus Canada (38 (10-81) versus 20 (4-178)). A lower number of pharmacists per hospital was observed in France (17 (11.5-35) versus 45 (18.9-76.8)), but a similar number of pharmacists were assigned to clinical trials (1.5 (1-3) versus 1.9 (0.2-17.4)). Institutional protocols represented the majority of paediatric clinical trials in France (61% (14-100) versus 25% (0-100)). Similar pharmacy support services were offered, but the majority of French respondents also offered help for institutional protocol development (91 versus 50% P = 0.063). The main issues associated with paediatric clinical research were absence of financial interest from the pharmaceutical industry, prohibitive cost versus profit ratio, small patient cohorts and the non-availability of the appropriate drug formulations. CONCLUSIONS: Difficulties related to pharmaceutical compounding were identified as the main hindrance to paediatric clinical research; particular attention should be paid to these details when setting up a paediatric trial.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".