Characterizing clinical heterogeneity in an inpatient service treating mental, substance use and concurrent disorders
Bibliographic record
Abstract
Patients diagnosed with concurrent disorders (CD)-comorbid substance use disorder with other psychiatric conditions-experience poorer clinical outcomes, and significant gaps remain in defining the optimal care path for treating CD. Toward this goal, the primary aim of this study was to characterize individual differences in substance use and psychiatric symptomology in an inpatient clinical sample using a person-centred approach. Admission assessment data from a private inpatient service treating mental disorders, substance use, and concurrent disorders was used (n = 177). Latent profile analysis (LPA) was performed to classify individuals into statistically distinct latent profiles based on their psychiatric symptoms and polysubstance use as covariates. LPA revealed four profiles. Profile 1 (20%) was identified as having low SUD and low psychiatric disorders, profile 2 (65%) was identified as having low SUD and high psychiatric disorders, profile 3 (8%) was characterized as high substance use and moderate psychiatric disorders and profile 4 (7%) was identified as the high SUD and high psychiatric disorders. The participants in the two profiles endorsing high SUDs, Profiles 3 and 4, showed significantly higher impulsivity in terms of higher positive urgency sensation-seeking scores compared to the other profiles and the highest use of cocaine/stimulants than the other two. Identifying clinical heterogeneity by classifying individuals into distinct profiles is a first step toward designing more targeted and personalized interventions in clinically complex inpatient populations.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".