Safety and effectiveness of <scp>NMDA</scp> receptor antagonists for depression: A multidisciplinary review
Bibliographic record
Abstract
Ketamine, an anesthetic available since 1970, and esketamine, its newer S-enantiomer, provide a novel approach for the treatment of depression and other psychiatric disorders. At subanesthetic doses, the two drugs, along with their older congener, phencyclidine (PCP), induce a transient, altered mental state by blocking the N-methyl-D-aspartate (NMDA) receptor for glutamate, the primary excitatory neurotransmitter in the mammalian central nervous system. This multidisciplinary review examines the pharmacology/direct effects on consciousness, effectiveness in depression and acute suicidality, and safety of these fast-acting NMDA antagonists. To capture the essence of 60 years of peer-reviewed literature, we used a semi-structured approach to the subtopics, each of which required a different search strategy. We review the evidence for the three primary reported benefits of the two clinical drugs when used for depression: success in difficult-to-treat patients, rapid onset of action within a day, and immediate effects on suicidality. Key safety issues include the evidence-and lack thereof-for the effects of repeatedly inducing this altered mental state, and whether an adequate safety margin exists to rule out the neurotoxic effects seen in animal studies. This review includes evidence from multiple sources that raise substantial questions about both safety and effectiveness of ketamine and esketamine for psychiatric 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".