Do Hospital and Community SSRI Usage Patterns in Children and Adolescents Match the Evidence?
Bibliographic record
Abstract
OBJECTIVES: 1) To review SSRI prescribing patterns for children and adolescents in our hospital and provincial prescription database and 2) To evaluate whether prescribing practices are consistent with expectations, based on published evidence and practice recommendations. METHODS: A PubMed online search was conducted to obtain all randomized controlled trials assessing efficacy of SSRI use in children and adolescents. The inpatient hospital pharmacy database at BC Children's Hospital (BCCH) and the BC Pharmacare database were used to identify all unique patients (under 19 years of age) seen in the inpatient department of psychiatry at BCCH or as outpatients in the province of BC receiving SSRI prescriptions between 2005-2009. RESULTS: Fluoxetine, citalopram, escitalopram and sertraline have evidence supporting their efficacy in the treatment of depressive disorders. Fluoxetine, fluvoxamine, sertraline, paroxetine and venlafaxine have evidence for use in the treatment of anxiety disorders. Between 2005-2009, BCCH inpatient data revealed that fluoxetine is the most frequently prescribed SSRI, followed by citalopram, sertraline, fluvoxamine, venlafaxine, paroxetine and escitalopram. In the community outpatients, fluoxetine was most frequently prescribed SSRI followed by citalopram, venlafaxine, sertraline, paroxetine, fluvoxamine and escitalopram. CONCLUSIONS: Prescribing patterns for SSRIs at BC Children's Hospital are consistent with the available evidence in the pediatric population. Furthermore, with the exception of citalopram, provincial outpatient and inpatient prescriptions appear to follow published national guidelines. Hospital SSRI usage more closely reflects the available literature than outpatient community usage does.
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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 | 0.012 | 0.092 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.013 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".