Unlicensed and off-label drug use in paediatrics in a mother-child tertiary care hospital
Why this work is in the frame
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Bibliographic record
Abstract
OBJECTIVE: To assess unlicensed and off-label drug use in a tertiary care paediatric hospital in Canada on a single day. METHODS: A cross-sectional study in a tertiary care paediatric hospital was conducted on one randomly selected day. Active prescriptions for children <18 years of age were analyzed. Unlicensed drug use was defined as the use of nonmarketed drugs in Canada or marketed drugs with pharmacy compounding. Off-label drug use was defined as the use of marketed drugs in Canada for an unapproved age group, indication, dosing, frequency and/or route of administration. Off-label drug uses associated with strong scientific support were analyzed using the Pediatric Dosage Handbook, 14th edition and Micromedex(®) Solutions. Number and proportion of unlicensed and off-label drug uses, and off-label drug uses associated with strong scientific support were measured. RESULTS: A total of 2145 drug prescriptions were extracted on March 5, 2014, for inclusion in the present study. The unlicensed drug use rate was 8.3% (57 unlicensed drug products; 75 nonmarketed drug prescriptions and 103 pharmacy compounding prescriptions) and the off-label drug use rate was 38.2% (161 substances; 819 prescriptions). Reasons for off-label drug use included unapproved age group (n=436 [53.2%]), dosing (n=226 [27.6%]), frequency (n=206 [25.2%]), indication (n=45 [5.5%]) and administration route (n=46 [5.6%]). Of the off-label drug prescriptions, 39.3% (n=322) were associated with strong scientific support. CONCLUSIONS: On a randomly selected day, 8.3% of prescriptions were unlicensed and 38.2% were off-label for children hospitalized at the authors' institution. Of off-label prescriptions, only 39.3% were associated with strong scientific support.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 it