Pharmacological Treatment of Adolescent Major Depression
Bibliographic record
Abstract
Antidepressant agents are widely prescribed for adolescents, although specific data regarding their efficacy in this age range are limited. The aims of the present article are to review research findings regarding the use of antidepressant drugs for adolescent depression and to discuss the main results in light of our clinical experience. Only 13 controlled trials on the use of antidepressant drugs for adolescent major depression are available in the literature. Six studies evaluated the efficacy of tricyclic antidepressants, yet they only included 196 adolescents altogether. Seven studies, including a total of 1,403 patients, evaluated the efficacy of three specific serotonin reuptake inhibitors: fluoxetine, paroxetine, and sertraline. Based on published data, serotonin reuptake inhibitors appear to be the first-line psychopharmacologic treatment for adolescent depression, as three compounds (fluoxetine, paroxetine, and sertraline) appeared to be effective in this indication. Conversely, all published studies failed to demonstrate that the tricyclic antidepressants were superior to placebo. Several questions remain open and are discussed: How should we use available scientific data in clinical practice? Are there nonspecific factors implicated in treatment response? Is there a serotonin hypothesis for juvenile depression? What are the priorities for future research?
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".