Troubling trends in prescribing for children
Bibliographic record
Abstract
Background: Off-label prescribing occurs when a practitioners prescribes a medication for either a condition or a population for which a regulatory body has not granted approval. Off-label prescribing can come with unintended negative consequences, such as safety concerns.\nMethods: We used systematic review methods to identify psychotropics being prescribed for young people in Canada. We then compared how these prescription practices align with the best available research evidence regarding the use of psychotropics in children and youth.\nResults: The data revealed striking increases in the number of antipsychotic prescriptions written and dispensed for young people with risperidone, quetiapine and olanzapine being prescribed most frequently. We also found that these medications were often prescribed for conditions, such as depression and anxiety disorders, for which there was neither regulatory approval nor high quality research evidence to support their use.\nConclusions: Off-label psychiatric prescribing comes with many risks. To help remedy this situation, more high-quality pediatric medication trials and more robust monitoring of drug safety are required. 
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".