Drug Treatment in Juvenile Depression – Is St. John's Wort a Safe and Effective Alternative?
Bibliographic record
Abstract
Analyses of juvenile depression studies with long established anti-depressants (tricyclic anti-depressants) have revealed discouragingly little benefit, while side effects have been profound. Modern anti-depressants like selective serotonin reuptake inhibitors seemed to solve part of this problem until they were found to be associated with an increased risk of suicidal attempts and ideation, hostile behaviour and self-harm, while meta-analyses have revealed only marginal therapeutic effects for the majority. Actually, no drug is unequivocally accepted as the gold-standard for young depressive patients. St. John's Wort (SJW) has been traditionally used in Europe to treat symptoms associated with juvenile depression. Close to 50 clinical studies performed over the last two decades have been presented as evidence that standardized SJW preparations are equally effective as synthetic anti-depressants in the treatment of mild to moderate depression in adults. Tolerability is excellent, but some relevant drug interactions have to be considered. Today, SJW is by far the most frequently prescribed medication for child and adolescent depression in Germany. Some pilot and observational studies from Germany, Canada and the US have delivered promising results. However, randomised controlled trials amongst this age group have yet to be carried out and are long overdue.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".