Key patterns and predictors of response to treatment for military veterans with post-traumatic stress disorder: a growth mixture modelling approach
Bibliographic record
Abstract
BACKGROUND: To determine the patterns and predictors of treatment response trajectories for veterans with post-traumatic stress disorder (PTSD). METHODS: Conditional latent growth mixture modelling was used to identify classes and predictors of class membership. In total, 2686 veterans treated for PTSD between 2002 and 2015 across 14 hospitals in Australia completed the PTSD Checklist at intake, discharge, and 3 and 9 months follow-up. Predictor variables included co-morbid mental health problems, relationship functioning, employment and compensation status. RESULTS: Five distinct classes were found: those with the most severe PTSD at intake separated into a relatively large class (32.5%) with small change, and a small class (3%) with a large change. Those with slightly less severe PTSD separated into one class comprising 49.9% of the total sample with large change effects, and a second class comprising 7.9% with extremely large treatment effects. The final class (6.7%) with least severe PTSD at intake also showed a large treatment effect. Of the multiple predictor variables, depression and guilt were the only two found to predict differences in response trajectories. CONCLUSIONS: These findings highlight the importance of assessing guilt and depression prior to treatment for PTSD, and for severe cases with co-morbid guilt and depression, considering an approach to trauma-focused therapy that specifically targets guilt and depression-related cognitions.
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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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".