Cluster B personality disorders and psychotropic medications: a focused analysis of trends and patterns across sex and age groups
Bibliographic record
Abstract
PURPOSE: This study investigated sex and age differences in patterns of psychotropic medication use before and after the initial diagnosis of Cluster B personality disorders (PDs) and analyzed trends over time. METHODS: Analyzing data from the Quebec Integrated Chronic Disease Surveillance System for individuals newly diagnosed with Cluster B PD (≥ 14 years) between 2002 and 2018 and under the provincial public drug plan, we calculated yearly and monthly proportions of individuals exposed to psychotropic medications during the year before and after their diagnosis by sex and age. Robust Poisson regression models assessed the association between sex and exposure to psychotropic medications after the diagnosis of Cluster B PD. RESULTS: Among 87,778 individuals with a first Cluster B PD diagnosis (mean age: 44.5 years; 57.5% women), the proportion of users increased post-diagnosis. Notably, after diagnosis, females were more likely to receive psychiatric medications (between 78.9% and 83.7% during the study period vs. 72.8% and 76.8%). Males were less likely than females to receive antidepressants (adjusted prevalence ratio (aPR): 0.83; 99% confidence interval (CI): 0.82-0.85) and anxiolytics (aPR: 0.86; 99%CI: 0.84-0.88), whereas they had higher exposure to antipsychotics (aPR: 1.04; 99%CI: 1.02-1.06) and ADHD medications (aPR: 1.14; 99%CI: 1.07-1.2). Age-specific trends showed increased ADHD medication use among younger patients (14-24 years), and anxiolytic use predominated in those aged ≥ 65 years. CONCLUSIONS: Psychotropic medication use was high among Cluster B PD patients, with differences in medication classes according to age and sex. The marked sex and age differences in psychotropic medication use among Cluster B PD patients underscore the need for a sex-sensitive and age-specific approach in psychiatric care.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.001 | 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".