Treatment Resistant Depression— Advances in Somatic Therapies
Bibliographic record
Abstract
BACKGROUND: The failure to achieve remission for patients with Major Depressive Disorder (MDD) represents a major public health concern. Inadequately treated depression is associated with higher rates of relapse, poorer quality of life, deleterious personal and societal economic ramifications, as well as increased mortality rates. Unfortunately, only a minority of patients achieves this goal with initial antidepressant treatment and by convention, failure to achieve response after two adequate trials of antidepressant therapy defines "Treatment Resistant Depression" (TRD). Furthermore, results from the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) group of studies suggest that approximately 50% of "real world" patients who meet criteria for MDD fail to achieve remission, even after four carefully monitored sequenced treatments. METHODS: Given these limitations of existing antidepressant medications alone and in combination, together with improved understanding of the neural circuitry of depression, it is not surprising that there is a renewed interest in neuromodulation strategies for TRD. RESULTS: The purpose of this article is to review the evidence for the inclusion of various non-pharmacological, neuromodulatory strategies for TRD. Specifically, information regarding the mechanism, tolerability and efficacy of electroconvulsive therapy (ECT), magnetic seizure therapy (MST), repetitive transcranial magnetic stimulation (rTMS), vagal nerve stimulation (VNS), and deep brain stimulation (DBS) in ameliorating TRD will be presented. CONCLUSIONS: Although these treatments are at various stages of clinical development, they represent a new frontier in expanding the treatment options available for individuals with TRD, as well as contributing to a better understanding the neurobiology of depressive disorders.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".