The identification, assessment and management of difficult-to-treat depression: An international consensus statement
Bibliographic record
Abstract
BACKGROUND: Many depressed patients are not able to achieve or sustain symptom remission despite serial treatment trials - often termed "treatment resistant depression". A broader, perhaps more empathic concept of "difficult-to-treat depression" (DTD) was considered. METHODS: A consensus group discussed the definition, clinical recognition, assessment and management implications of the DTD heuristic. RESULTS: The group proposed that DTD be defined as "depression that continues to cause significant burden despite usual treatment efforts". All depression management should include a thorough initial assessment. When DTD is recognized, a regular reassessment that employs a multi-dimensional framework to identify addressable barriers to successful treatment (including patient-, illness- and treatment-related factors) is advised, along with specific recommendations for addressing these factors. The emphasis of treatment, in the first instance, shifts from a goal of remission to optimal symptom control, daily psychosocial functional and quality of life, based on a patient-centred approach with shared decision-making to enhance the timely consideration of all treatment options (including pharmacotherapy, psychotherapy, neurostimulation, etc.) to optimize outcomes when sustained remission is elusive. LIMITATIONS: The recommended definition and management of DTD is based largely on expert consensus. While DTD would seem to have clinical utility, its specificity and objectivity may be insufficient to define clinical populations for regulatory trial purposes, though DTD could define populations for service provision or phase 4 trials. CONCLUSIONS: DTD provides a clinically useful conceptualization that implies a search for and remediation of specific patient-, illness- and treatment obstacles to optimizing outcomes of relevance to 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 | 0.130 | 0.103 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.014 | 0.010 |
| Research integrity | 0.016 | 0.020 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".