Retrospective Analysis of Emerging Drugs Use In A Quebec Women And Children University Hospital And Perspectives For Safe And Optimal Drug Use
Bibliographic record
Abstract
BACKGROUND: Only few medicines are licensed for children. The use of emerging drugs (unmarketed drug, off-label drug with poorly documented use, and/or costly drugs) might represent an essential alternative for pediatric patients. OBJECTIVES: The objective of the study was to assess emerging drug uses rate and profile in our women's and children's centre to support the implementation of an appropriate policy. METHODS: We identified retrospectively emerging drugs used between 2013-01-01 and 2014-02-28, using computerized pharmacist software extraction of drugs used. Conventional oncologic drugs were excluded. Retrospective analysis of medical charts for patients who received an emerging drug and literature review for each drug were performed to determine efficacy and safety endpoints. Median delays between first intention and final decision to use the drug and between final decision and first administration were calculated. Proportion of patients who experienced a positive evolution under treatment or a side effect possibly related to the drug was calculated. RESULTS: A total of 26 emerging drugs were identified (89 patients, 99 uses). Median treatment duration was 66 days [1-1435]. Median delay between first evocation and final decision to use the drug was 2 days [0-333] and 0 day [0-404] between final decision and first administration. 52/99 (53%) of patients experienced a positive evolution under treatment and 26/99 (26%) experienced a side effect possibly related to emerging drug use. CONCLUSIONS: This study allowed us to describe emerging drug uses in a women and children tertiary hospital. It led to the implementation of a local emerging drug use policy ensuring optimal and safe use of these drugs. There is a significant number of emerging drugs used in pediatric which shows positive improvement in 56% of patients.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".