A review of escitalopram and citalopram in child and adolescent depression.
Bibliographic record
Abstract
OBJECTIVE: To review the basic pharmacology and published literature regarding escitalopram and citalopram in child and adolescent depression. METHODS: A LITERATURE REVIEW WAS CONDUCTED USING THE SEARCH TERMS: 'escitalopram', 'citalopram', 'depression', 'randomized controlled trial', 'open label trial' and limits set to: Human trials, English Language and All Child (Age 0-18). Additional articles were identified from reference information and poster presentation data. RESULTS: Three prospective, randomized controlled trials (RCT) were found for escitalopram in pediatric depression, and two RCTs were found for citalopram. One RCT each for escitalopram and citalopram showed superiority over placebo on the primary out come measure. Adverse effects in escitalopram and citalopram trials were generally mild to moderate. Suicidality was not assessed systematically in all RCTs reviewed, but did not appear to be elevated over placebo in escitalopram RCTs. One trial reported numerically higher suicide related events for citalopram compared to placebo (14 vs. 5, p=0.06). CONCLUSION: At present, escitalopram and citalopram should be considered a second-line option for adolescent depression. The US Food and Drug Administration approval of escitalopram for treatment of adolescent depression was based on a single positive RCT. This is less evidence than typically required for approval of a drug for a new indication.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".