Canadian Network for Mood and Anxiety Treatments (Canmat) Consensus Recommendations for Functional Outcomes in Major Depressive Disorder
Bibliographic record
Abstract
BACKGROUND: Functional recovery is increasingly recognized as a priority in the treatment of major depressive disorder (MDD), by both clinicians and patients. However, symptom improvement remains the focus of traditional clinical trials for MDD and of the regulatory approval process for new medications and other interventions. Many studies have shown that functional outcomes do not always correspond to symptom-based outcomes. METHODS: Representatives from clinical practice, professional societies, academia, industry, and government were invited by the Canadian Network for Mood and Anxiety Treatments to develop recommendations for the conceptualization and measurement of functional outcomes in clinical trials of MDD. RESULTS: Definitions and conceptual frameworks to guide assessment of functioning are described, as well as research methodology applicable to the broad spectrum of treatments for MDD. Examples are given for validated instruments, including patient-reported outcome measures. Strategies for knowledge translation and dissemination are suggested and consensus recommendations summarized. CONCLUSIONS: As the societal burden and financial costs of MDD continue to escalate, so does the need for evidence-based and cost-effective interventions that demonstrate improvement in functioning. Routine assessment of functional outcomes will benefit not only individuals with MDD but also diverse stakeholders concerned about the efficacy and cost-effectiveness of interventions.
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 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 | 0.039 | 0.097 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.012 | 0.004 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.013 | 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".