Increased Prevalence of Childhood Complex Trauma in Comorbid Posttraumatic Stress Disorder and Substance Use Disorders Compared to Either Disorder Alone: A Systematic Review
Bibliographic record
Abstract
OBJECTIVE: Childhood complex trauma (CCT) prevalence among individuals with comorbid posttraumatic stress disorder (PTSD) and substance use disorder (SUD) is unknown. We conducted a meta-analysis to compare CCT prevalence in samples of PTSD alone, SUD alone and comorbid PTSD+SUD. METHOD: A systematic review of PTSD, CCT and SUD literature was conducted using online databases. Binary outcome meta-analytic models were fitted comparing CCT prevalence in comorbid PTSD + SUD to PTSD and SUD only. RESULTS: Seven studies were included, and estimates for CCT prevalence were higher, on average, among individuals with comorbid PTSD+SUD (35%-78%) compared to PTSD alone (4%-70%) and SUD alone (2%-65%). A meta-analysis of four studies indicated individuals with comorbid PTSD+SUD were 18% more likely to have experienced CCT compared to individuals with PTSD only (RR = 1.18, 95% CI [1.13, 1.25]) and 24% more likely compared to individuals with SUD only (RR = 1.24, 95% CI [1.20, 1.29]). CONCLUSIONS: Further research is needed to establish a more accurate prevalence rate for individuals with comorbid PTSD and SUD. Evidence of greater CCT prevalence will inform research study design and clinical targets during treatment for individuals with comorbid PTSD and SUD.
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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.012 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".