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Record W4388942433 · doi:10.3389/fpsyt.2023.1243511

Psychotropic medication use pre and post-diagnosis of cluster B personality disorder: a Quebec’s health services register cohort

2023· article· en· W4388942433 on OpenAlexaffabout
Carlotta Lunghi, Lionel Cailhol, Victoria Massamba, Elhadji Anassour Laouan Sidi, Caroline Sirois, Elham Rahme, Louis Rochette, Suzane Renaud, Évens Villeneuve, Marion Koch, Robert Biskin, Cathy Martineau, Philippe Vincent, Pierre David, Alain Lesage

Bibliographic record

VenueFrontiers in Psychiatry · 2023
Typearticle
Languageen
FieldMedicine
TopicBipolar Disorder and Treatment
Canadian institutionsCentre Intégré de Santé et de Services Sociaux des LaurentidesMcGill UniversityInstitut Universitaire en Santé Mentale de QuébecUniversité de MontréalUniversité LavalInstitut National de Santé Publique du QuébecUniversité du Québec à Rimouski
Fundersnot available
KeywordsMedicinePsychiatryPoisson regressionCohortAnxietyMoodCohort studyPopulationMental healthCluster (spacecraft)Depression (economics)ConfoundingComorbidityObservational studyPediatricsInternal medicineEnvironmental health

Abstract

fetched live from OpenAlex

Background: Cluster B personality disorders (PDs) are considered some of the most severe mental health conditions. Scarce evidence exists about the real-world utilization of psychotropics for cluster B PD individuals. Objective: We aimed to uncover trends and patterns of psychotropic medication use among individuals diagnosed with cluster B PD in the year before and after their diagnosis and to identify factors associated with medication use in a large cohort of individuals newly diagnosed with cluster B PDs. Methods: We conducted a population-based observational study using Quebec's health services register. We identified Quebec residents aged ≥14 years and insured with the provincial drug plan with a first diagnosis of cluster B PD recorded between April 1, 2002, and March 31, 2019. Cluster B PD was defined with ICD-9/10 diagnostic codes. We retrieved all claims for the main psychotropic medication classes: antipsychotics, antidepressants, anxiolytics, mood stabilizers, and attention-deficit/hyperactivity disorder (ADHD) medications. We calculated the proportion of individuals exposed to these medication classes and analyzed trends over the years using robust Poisson regression models, adjusting for potential confounders. We used robust Poisson regression to identify factors associated with medication class use. Results: We identified 87,778 new cases of cluster B PD, with a mean age of 44.5 years; 57.5% were women. Most frequent psychiatric comorbidities in the five years before cluster B PD diagnosis were depression (50.9%), anxiety (49.7%), and psychotic disorders (37.5%). Most individuals (71.0%) received at least one psychotropic during the year before cluster B PD diagnosis, and 78.5% received at least one of these medications in the subsequent year. The proportion of users increased after the diagnosis for antidepressants (51.6-54.7%), antipsychotics (35.9-45.2%), mood stabilizers (14.8-17.0%), and ADHD medications (5.1-5.9%), and remained relatively stable for anxiolytics (41.4-41.7%). Trends over time showed statistically significant increased use of antipsychotics and ADHD medications, decreased use of anxiolytics and mood stabilizers, and a stable use of antidepressants. Conclusion: Psychotropic medication use is highly prevalent among cluster B PD individuals. We observed an increase in medication use in the months following the diagnosis, particularly for antipsychotics, antidepressants, and mood stabilizers.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.035
Threshold uncertainty score0.078

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.003
Science and technology studies0.0020.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.008
GPT teacher head0.275
Teacher spread0.268 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations8
Published2023
Admission routes2
Has abstractyes

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