What’s in a name? A data-driven method to identify optimal psychotherapy classifications to advance treatment research on co-occurring PTSD and substance use disorders
Bibliographic record
Abstract
Background/Objective: The present study leveraged the expertise of an international group of posttraumatic stress and substance use disorder (PTSD+SUD) intervention researchers to identify which methods of categorizing interventions which target SUD, PTSD, or PTSD+SUD for populations with both PTSD+SUD may be optimal for advancing future systematic reviews, meta-analyses, and comparative effectiveness studies which strive to compare effects across a broad variety of psychotherapy types. Method: A two-step process was used to evaluate the categorization terminology. First, we searched the literature for pre-existing categories of PTSD+SUD interventions from PTSD+SUD clinical trials, systematic and literature reviews. Then, we surveyed international trauma and substance use subject matter experts about their opinions on pre-existing intervention categorization and ideal categorization nomenclature. Results: = 27) revealed that interventions for PTSD+SUD can be classified in many ways that appear to overlap highly with one another. Many experts (11/27; 41%) selected the categories of 'trauma-focused and non-trauma focused' as an optimal way to distinguish treatment types. Although several experts reinforced this point during the subsequent meeting, it became clear that no method of categorizing treatments is without flaws. Conclusion: One possible categorization (trauma-focused/non-trauma focused) was identified. Revised language and nomenclature for classification of PTSD+SUD treatments are needed in order to accommodate the needs of this advancing field.
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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 | 0.216 | 0.469 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.013 |
| Bibliometrics | 0.022 | 0.019 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".