The Integrative Management of Treatment-Resistant Depression: A Comprehensive Review and Perspectives
Bibliographic record
Abstract
BACKGROUND: Major depressive disorder is a prevalent and disabling illness. Notwithstanding numerous advances in the pharmacological treatment of depression, approximately 70% of patients do not remit after first-line antidepressant treatment. METHODS: The MEDLINE/PubMed, EMBASE and ClinicalTrials.gov electronic databases were searched from inception to October 1, 2013, for randomized controlled trials (RCT), relevant open-label trials, meta-analyses and ongoing trials of pharmacological and psychotherapeutic approaches to treatment-resistant depression (TRD). RESULTS: Switching to a different antidepressant is a useful option following nonresponse to a first-line agent. Although widely used in clinical practice, there is limited evidence to support antidepressant combination for TRD. Notwithstanding evidence for lithium or T3 augmentation to be successful in TRD, most studies were carried out when participants were treated with tricyclic antidepressants (TCA). Of the available strategies to augment the response to new-generation antidepressants, the use of some atypical antipsychotics is best supported by evidence. Several novel therapeutic options are currently discussed. Evidence suggests that cognitive therapy (CT) is an effective strategy for TRD. CONCLUSIONS: The success of switching to a different antidepressant following a first-line agent is supported by evidence, but there is limited evidence for effective combination strategies. Lithium and T3 augmentation of TCA have the strongest evidence base for successful treatment of TRD. The use of augmentation of newer-generation antidepressants with atypical antipsychotics is supported by a growing evidence base. Current evidence supports CT as an effective strategy for TRD. There is a need for additional large-scale RCT of TRD. The development of new antidepressants targeting novel pathways opens a promising perspective for the management 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".