Intravenous Ketamine “Burst” for Refractory Depression in a Patient with Advanced Cancer
Bibliographic record
Abstract
Major Depressive Disorder (MDD) is prevalent in patients with advanced cancer, and can have a negative impact on patients' quality of life. Available antidepressants, often have delayed benefits of several weeks, and therefore are of limited utility in the palliative care setting. Psychostimulants provide more rapid onset of action, but frequently require dose escalation because of problems with tolerance. There is a growing body of evidence that N-methyl-D-aspartate (NMDA) receptor antagonists, such as ketamine, signficantly and rapidly improve depressive symptoms in treatment resistant depression. However, studies conducted to date have not included advanced cancer patients. We report on a case where intravenous ketamine 'bursts' (0.5 mg/kg infused over 60 minutes) were used to treat an intractable MDD in a patient with metastatic prostate cancer. Initial positive response was not sustained, and response to a repeat treatment was even more transient. Adverse effects were mild and self-limiting. We conclude that a well-designed, randomized study of IV ketamine "bursts" in cancer patients suffering from depression is needed to further establish the role and appropriate dosing of ketamine in this patient population. Given that ketamine can be used as an adjuvant for difficult pain syndromes in cancer patients, it would be of interest to assess its impact on the mood in patients receiving this treatment for pain.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".