Efficacy of Fifteen Emerging Interventions for the Treatment of Posttraumatic Stress Disorder: A Systematic Review
Bibliographic record
Abstract
Although there is an abundance of novel interventions for the treatment of posttraumatic stress disorder (PTSD), often their efficacy remains unknown. This systematic review assessed the evidence for 15 new or novel interventions for the treatment of PTSD. Studies that investigated changes to PTSD symptoms following the delivery of any 1 of the 15 interventions of interest were identified through systematic literature searches. There were 19 studies that met the inclusion criteria for this study. Eligible studies were assessed against methodological quality criteria and data were extracted. The majority of the 19 studies were of poor quality, hampered by methodological limitations, such as small sample sizes and lack of control group. There were 4 interventions, however, stemming from a mind-body philosophy (acupuncture, emotional freedom technique, mantra-based meditation, and yoga) that had moderate quality evidence from mostly small- to moderate-sized randomized controlled trials. The active components, however, of these promising emerging interventions and how they related to or were distinct from established treatments remain unclear. The majority of emerging interventions for the treatment of PTSD currently have an insufficient level of evidence supporting their efficacy, despite their increasing popularity. Further well-designed controlled trials of emerging interventions for PTSD are required.
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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".