Definition, Assessment, and Staging of Treatment—Resistant Refractory Major Depression: A Review of Current Concepts and Methods
Bibliographic record
Abstract
OBJECTIVE: Up to 15% of depression patients eventually present with treatment-resistant or refractory depression (TRD), a condition that causes significant social and economic burdens. Our paper aims to summarize the current medical literature on the conceptual and methodologic issues involved in the definition, assessment, and staging of TRD. METHOD: We reviewed the recently published medical literature to identify papers that specifically discuss TRD. For this, we searched MEDLINE, EMBASE, and PsycINFO for potentially relevant English-language articles published between January 1996 and June 2006. RESULTS: Recent methodologic and conceptual advances have contributed to the achievement of an acceptable level of theoretical consensus on the general meaning of TRD. Accordingly, depression is usually considered resistant or refractory when at least 2 trials with antidepressants from different pharmacologic classes (adequate in terms of dosage, duration, and compliance) fail to produce a significant clinical improvement. Regarding diagnostic assessments, an accurate and systematic evaluation should be made to elicit the potential role of several contributing factors, such as medical and psychiatric comorbidity. CONCLUSION: Recently, 3 staging methods for TRD have been described, but they currently require extensive empirical support. Future research on TRD should include prospective studies addressing the validity of the proposed criteria, the impact of depression comorbid with other psychiatric disorders and (or) physical conditions, and the possible predictors o treatment outcome. There is an important and clear need for studies that empirically test current definitions, assessment strategies, and staging methods of TRD.
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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 | 0.023 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.016 | 0.010 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".