Aripiprazole Augmentation in the Treatment of Military-Related PTSD with Major Depression: a retrospective chart review
Bibliographic record
Abstract
BACKGROUND: In this chart review, we attempted to evaluate the benefits of adding aripiprazole in veterans with military-related PTSD and comorbid depression, who had been minimally or partially responsive to their existing medications. METHODS: A retrospective chart review of patients who received an open-label, flexible-dose, 12- week course of adjunctive aripiprazole was conducted in 27 military veterans meeting DSM-IV criteria for PTSD and comorbid major depression. Concomitant psychiatric medications continued unchanged, except for other antipsychotics which were discontinued prior to initiating aripiprazole. The primary outcome variable was a change from baseline in the PTSD checklist-military version (PCL-M) and the Beck Depression Inventory (BDI-II). RESULTS: PTSD severity (Total PCL scores) decreased from 56.11 at baseline to 46.85 at 12-weeks (p < 0.0001 from Wilcoxon signed rank test) and the depression severity decreased from 30.44 at baseline to 20.67 at 12-weeks (p < 0.0001 from Wilcoxon signed rank test). Thirty seven percent (10/27) were considered responders, as defined by a decrease in total PCL scores of at least 20 percent and 19% (5/27) were considered as responders as defined by a decrease in total BDI score of at least 50%. CONCLUSIONS: The addition of aripiprazole contributed to a reduction in both PTSD and depression symptomatology in a population that has traditionally demonstrated poor pharmacological response. Further investigations, including double-blind, placebo-controlled studies, are essential to confirm and further demonstrate the benefit of aripiprazole augmentation in the treatment of military related PTSD.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".