Does body mass index predict response to intravenous ketamine treatment in adults with major depressive and bipolar disorder? Results from the Canadian Rapid Treatment Center of Excellence
Bibliographic record
Abstract
BACKGROUND: Higher body mass index (BMI) has been found to predict greater antidepressant response to intravenous (IV) ketamine treatment. We evaluated the association between BMI and response to repeat-dose IV ketamine in patients with treatment-resistant depression (TRD). METHODS: Adults (N = 230) with TRD received four infusions of IV ketamine at a community-based clinic. Changes in symptoms of depression (ie, Quick Inventory for Depressive Symptomatology-Self-Report 16; QIDS-SR16), suicidal ideation (SI; ie, QIDS-SR16 SI item), anxiety (ie, Generalized Anxiety Disorder-7 Scale), anhedonic severity (ie, Snaith-Hamilton Pleasure Scale), and functioning (ie, Sheehan Disability Scale) following infusions were evaluated. Participants were stratified by BMI as normal (18.0-24.9 kg/m2; n = 72), overweight (25-29.9 kg/m2; n = 76), obese I (30-34.9 kg/m2; n = 47), or obese II (≥35.0 kg/m2; n = 35). RESULTS: Similar antidepressant effects with repeat-dose ketamine were reported between BMI groups (P = .261). In addition, categorical partial response (P = .149), response (P = .526), and remission (P = .232) rates were similar between the four BMI groups. CONCLUSIONS: The findings are limited by the observational, open-label design of this retrospective analysis. Pretreatment BMI did not predict response to IV ketamine, which was effective regardless of BMI.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".