Clinical Guidelines for the Treatment of Depressive Disorders VII. Comorbidity
Bibliographic record
Abstract
Background: The Canadian Psychiatric Association and the Canadian Network for Mood and Anxiety Treatments partnered to produce clinical guidelines for psychiatrists for the treatment of depressive disorders. Methods: A standard guidelines development process was followed. Relevant literature was identified using a computerized Medline search supplemented by review of bibliographies. Operational criteria were used to rate the quality of scientific evidence, and the line of treatment recommendations included consensus clinical opinion. This section, on Axis I, Axis II, and Axis III comorbidity, is 1 of 7 articles that were drafted and reviewed by clinicians. Revised drafts underwent national and international expert peer review. Results: Comorbid depression on Axis I is particularly prevalent in patients with anxiety disorders, substance use disorders, and eating disorders, but it also occurs in patients with schizophrenia, attention-deficit hyperactivity disorder (ADHD), and dementia. Depressive comorbidity has implications for assessment, management, and outcome. The relation between depression and personality disorders is complex. Patients with this comorbidity often require longer, more intense, and multimodal therapies. Depression is also prevalent in medical illnesses, requires careful diagnosis, and responds to standard antidepressant treatments. Conclusions: Comorbidity can influence the course and outcome of both associated conditions. Depression-specific psychotherapy and/or pharmacotherapy should be considered when comorbid depression is diagnosed.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.026 | 0.014 |
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".