Unlicensed and off-label drug use in paediatrics in a mother-child tertiary care hospital
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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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 | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".