Accelerated Resolution Therapy (ART) for the treatment of posttraumatic stress disorder in adults: A systematic review
Bibliographic record
Abstract
Accelerated Resolution Therapy (ART), developed in 2008, uses techniques such as rapid eye movement, in vivo exposure, and image rescripting to recondition stressful memories, and reduce physical and emotional reactions to traumatic memories. There is considerable interest in evidence-based treatments for post-traumatic stress disorder (PTSD). This is the first systematic review examining the efficacy of ART for the treatment of PTSD among adults. We searched MEDLINE, PsycINFO, Embase, CINAHL, Scopus, trial registries, and government and private websites for citations published before October 2023. Studies that reported on the effect of ART for PTSD among adults were included. Meta-analyses could not be undertaken due to heterogeneity in study designs and an insufficient number of studies with a low risk of bias. Risk of bias was assessed, and findings synthesized following the synthesis without meta-analysis (SWiM) guidelines. Of the 112 records screened, five studies (Nenrolled = 337; Ncompleted = 250) and six reports of studies met criteria for inclusion. Included studies reported a significant reduction in symptoms of PTSD from pre- to post-intervention, d = 1.12 to 3.28. Significant reductions were also reported in symptoms of depression, mental distress, anxiety, and sleep dysfunction. ART shows some promise as a time-efficient clinical treatment for symptoms of PTSD in adults; however, more high-quality studies are needed.
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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".