The Canadian Network for Mood and Anxiety Treatments (Canmat) Task Force Recommendations for the Management of Patients with Mood Disorders and Comorbid Personality Disorders
Bibliographic record
Abstract
Background The association between mood disorders and personality disorders (PDs) is complicated clinically, conceptually, and neurobiologically. There is a need for recommendations to assist clinicians in treating these frequently encountered patients. Methods The literature was reviewed with the purpose of identifying clinically relevant themes. MedLine searches were supplemented with manual review of the references in relevant papers. From the extant evidence, consensus-based recommendations for clinical practice were developed. Results Key issues were identified with regards to the overlap of PDs and mood disorders, including whether certain personality features predispose to mood disorders, whether PDs can reliably be recognized if there is an Axis I disorder present, whether personality disturbances arise as a consequence or are a forme fruste of mood disorders, and whether personality traits or disorders modify treatment responsiveness and outcome of mood disorders. Conclusion This paper describes consensus-based clinical recommendations that arise from a consideration of how signals from the literature can impact clinical practice in the treatment of patients with comorbid mood and personality pathology. Additional treatment studies of patients with the comorbid conditions are required to further inform clinical practice.
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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 | 0.024 | 0.064 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.010 | 0.003 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".