Predictors of response to intranasal ketamine in patients hospitalized for treatment-resistant depression
Bibliographic record
Abstract
Background Ketamine is recognized as an effective treatment for major depressive disorder but there remains a need to identify predictors of response to ketamine therapy. Methods This naturalistic study was a retrospective analysis of data from 48 patients with treatment-resistant depression who received up to four doses of intranasal ketamine in an acute psychiatric hospital setting. Baseline demographic and clinical variables were examined as potential predictors of response along with treatment-emergent dissociation and blood pressure changes. Response was defined as a depression score decrease of 50% or more from baseline measured with the Hamilton Depression Rating Scale or the Montgomery–Åsberg Depression Rating Scale. Results The response rate was significantly lower in patients diagnosed with a personality disorder, the most common of which was borderline personality disorder. In contrast, baseline scores on patient-reported questionnaires assessing maladaptive personality traits were not associated with lower response rates. Adverse childhood experiences and dissociation during treatment were associated with higher response rates. Conclusions Patients with significant childhood trauma appeared to benefit more from ketamine treatment, consistent with previous research. More research to determine if personality disorders influence ketamine response is needed and should distinguish clinical diagnoses from those derived solely from patient-reported questionnaires. Treatment-emergent dissociation could be a predictor of response to intranasal ketamine but requires further study.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".