Treatment‐resistant major depressive disorder: Canadian expert consensus on definition and assessment
Bibliographic record
Abstract
BACKGROUND: Treatment-resistant depression (TRD) is a debilitating chronic mental illness that confers increased morbidity and mortality, decreases the quality of life, impairs occupational, social, and offspring development, and translates into increased costs on the healthcare system. The goal of this study is to reach an agreement on the concept, definition, staging model, and assessment of TRD. METHODS: This study involved a review of the literature and a modified Delphi process for consensus agreement. The Appraisal of Guidelines for Research & Evaluation II guidelines were followed for the literature appraisal. Literature was assessed for quality and strength of evidence using the grading, assessment, development, and evaluations system. Canadian national experts in depression were invited for the modified Delphi process based on their prior clinical and research expertize. Survey items were considered to have reached a consensus if 80% or more of the experts supported the statement. RESULTS: Fourteen Canadian experts were recruited for three rounds of surveys to reach a consensus on a total of 27 items. Experts agreed that a dimensional definition for treatment resistance was a useful concept to describe the heterogeneity of this illness. The use of staging models and clinical scales was recommended in evaluating depression. Risk factors and comorbidities were identified as potential predictors for treatment resistance. CONCLUSIONS: TRD is a meaningful concept both for clinical practice and research. An operational definition for TRD will allow for opportunities to improve the validity of predictors and therapeutic options for these patients.
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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 | 0.088 | 0.124 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".