Treatment-Resistant Depression – What is the Effective Maintenance Treatment
Bibliographic record
Abstract
Introduction Treatment-resistant depression (TRD) presents a significant challenge in clinical practice. Besides antidepressant medications, neurostimulation methods (ECT, rTMS) and ketamine are viable treatment options. Objectives To objectively evaluate the real effectiveness of treatments within interventional psychiatry in the maintenance treatment. Methods The extensive literature review of the efficacy of ECT, rTMS, and ketamine treatment in the maintenance treatment of TRD and the author’s clinical and research experience will be included in this presentation. Results Neurostimulation, particularly ECT and ketamine treatment are usually effective treatments for patients with TRD. However, both of these treatment modalities do not have sustained benefits and after discontinuing treatment the majority of patients relapse. Ketamine has rapid therapeutic effects in depression, but these effects are short-lived. Continuation treatment with ketamine in the form of intranasal ketamine is an option, but concerns over cognitive impairment, interstitial cystitis and significant addictive potential related to longer use of ketamine are significant limiting factors. rTMS is a first-line treatment option for patients with TRD according to the Canadian CANMAT guidelines. However, the majority of patients may relapse following the course of rTMS. The maintenance rTMS over an extended period of time is usually not feasible as it may significantly affect the waiting time for newly referred patients. Portable TMS machine for home use would be an alternative option for a limited number of patients. Conclusions Maintenance treatment has been always a big clinical challenge in mood disorder psychiatry. Only well-established multimodal treatment is a realistic option for getting long-term benefits in treating patients with TRD. Disclosure No significant relationships.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".