A Randomized Controlled Trial of Antidepressant Continuation for Major Depression Following Traumatic Brain Injury
Bibliographic record
Abstract
OBJECTIVE: This study examines whether continuation therapy with citalopram can prevent a relapse following remission of major depression due to traumatic brain injury. METHOD: After 65 subjects with DSM-IV-diagnosed major depression following traumatic brain injury were treated with open-label citalopram (20 mg to 50 mg/d), 25 subjects (38.5%) met criteria for remission. Of those, 21 (84.0%) were randomly assigned to either same-dose citalopram or placebo and followed monthly over 40 weeks. Remission was defined as a Hamilton Depression Rating Scale (HDRS) score of ≤ 7 or a Clinical Global Impressions-Improvement rating of "much improved" or better. The main outcome variable was the presence of relapse, as defined by meeting criteria for major depressive episode according to the DSM-IV and an HDRS score ≥ 16. Data were collected from February 16, 2005, to May 5, 2008. RESULTS: Ten subjects were randomly assigned to citalopram and 11 to placebo. There were 3 dropouts, including 1 for adverse drug effects (diarrhea). Relapse occurred in 11 subjects (52.4%), with a mean ± SD time to relapse of 23.52 ± 16.6 weeks. The groups did not differ in relapse rates (drug: 50.0% [5/10] vs placebo: 54.5% [6/11], Fisher exact test, P = .835) or time to relapse (log rank test χ² = 0.148, P = .700). CONCLUSIONS: The present study suggests important limitations of continuation pharmacotherapy in the prevention of relapse of major depression following traumatic brain injury. TRIAL REGISTRATION: clinicaltrials.gov Identifier: NCT00162916.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".