The efficacy and safety of adjunctive intranasal esketamine treatment in major depressive disorder: a systematic review and meta-analysis
Bibliographic record
Abstract
INTRODUCTION: Intranasal (IN) esketamine represents an innovative treatment for individuals with treatment resistant depression and depression with suicidal ideation and behavior. Herein, we synthesize extant long-term studies (≥ 4 weeks) regarding this treatment. RESEARCH DESIGN AND METHODS: The interventional studies of IN esketamine in patients with depression having a study period of at least four weeks were included for our synthesis. A meta-analysis was undertaken for the efficacy and safety parameters of adjunctive IN esketamine vs IN placebo with an oral antidepressant. The data excluded from meta-analysis were synthesized narratively. RESULTS: After pooling data from seven randomized controlled trials, treatment with adjunctive IN esketamine vs IN placebo was safe overall, and more effective at decreasing depressive symptoms (d = -0.239; 95%CI = -0.335,-0.142;p < 0.0001), with higher response (RR = 1.221; 95% CI = 1.055,1.428; p = 0.017) and remission (RR = 1.366; 95% CI = 1.182,1.578; p < 0.0001) rates. The year-long trials showed that treatment with adjunctive IN esketamine led to lower relapse rates with no considerable long-term side effects. CONCLUSION: Intranasal esketamine was demonstrated to be safe, well tolerated, and rapidly effective in individuals with treatment resistant depression, suicidal ideation, and suicidal behavior.
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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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.031 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".