Pre-treatment Pain Symptoms Influence Antidepressant Response to Ketamine in Depressive Patients
Bibliographic record
Abstract
Background Pain strongly coexists with depression. Ketamine has great analgesic and antidepressant effects, acting as a promising role in treating depression with pain. Few studies have evaluated impact of pain symptoms on antidepressant effect of ketamine infusions. Thus, present study investigated whether pain symptoms in individuals with depression moderate response to ketamine. Methods One hundred and four individuals with major depressive disorder and bipolar depression received six intravenous infusions of ketamine. The Montgomery–Åsberg Depression Rating Scale (MADRS) was administered at baseline, the next morning after each infusion and 2 weeks (Day 26) after the last infusion. Pain symptoms were collected at baseline using the short-form McGill Pain Questionnaire (SF-MPQ). Results The prevalence of pain in patients with depression was 48.8%. Mix model analyses showed that pre-treatment pain symptoms assessed by each domain of SF-MPQ significantly moderated antidepressant response to six infusions of ketamine from baseline to day 26 (all p < 0.05). Then follow-up simple slopes analyses suggested that all patients across groups showed a significant symptomatic improvement after ketamine infusions (all p < 0.05), and patients with severe pain (across all domains of SF-MPQ) had greater improvement in depressive symptoms than those with mild pain or non-pain (all p < 0.05). Conclusion A significant and rapid improvement in depressive symptoms was observed in patients with depression and pain after ketamine treatment. Ketamine may be a novel and promising antidepressant preferentially for the therapy of depression with severe 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".