Using Latent Profile Analysis to Characterize Clinical Heterogeneity and Impulsivity in a Large Residential Addiction Treatment Program
Bibliographic record
Abstract
Objectives: Clinical heterogeneity among patients in addiction treatment settings represents a challenge as most of the treatment programs are designed to treat substance use disorders (SUD) generally rather than offering more tailored approaches addressing individual patient needs. Systematic characterization of clinical heterogeneity may permit more individualized care paths toward improving outcomes. Methods: Data were collected from a large inpatient SUD treatment program between April 2018 and March 2020 (n = 1519). Latent profile analysis (LPA) was applied to identify latent clusters based on differences in substance use and co-occurring depression, anxiety, and post-traumatic stress disorder. Results: Five distinct profiles emerged: Profile 1 (38%) exhibited the lowest substance use and lowest psychiatric severity (Overall Low); Profile 2 (39%) exhibited high alcohol and psychiatric severity; Profile 3 (13%) exhibited high opioid severity and low psychiatric severity. Profile 4 (8%) exhibited high cannabis use and high psychiatric severity, and profile 5 (3%) exhibited high polysubstance use other than alcohol and cannabis use. The latter two profiles were younger and exhibited higher self-regulatory deficits. The (High Alc/high psych) and the (High Cann/Psych) profiles exhibited differentially higher psychiatric severity. Profiles showing high polysubstance use, as well as high cannabis use and high psychiatric severity, showed significantly higher impulsive behavior than the others. Conclusions: LPA revealed five clusters of patients varying substantially in terms of SUD and psychiatric severity. Addressing common features of clinical heterogeneity for tailored care paths in a personalized treatment approach may improve treatment outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".