Esketamine Nasal Spray vs Quetiapine Extended-Release
Bibliographic record
Abstract
This post hoc analysis of the ESCAPE-TRD trial compared work productivity loss (WPL) and related costs among patients with treatment-resistant depression (TRD) receiving esketamine nasal spray or quetiapine extended release in combination with an oral antidepressant. Adults with TRD randomized to receive esketamine (56/84 mg) or quetiapine (150-300 mg) combined with ongoing antidepressant therapy were included. WPL was assessed using the Work Productivity and Activity Impairment questionnaire. Least squares (LS) mean WPL change versus baseline (treatment initiation date), and LS mean differences (MDs) between esketamine and quetiapine cohorts were reported at weeks 8-32 of treatment using mixed models for repeated measurements. Per patient productivity cost savings were estimated using mean 2021 weekly wages from US Bureau of Labor Statistics. The esketamine cohort included 165 patients, and quetiapine cohort included 156 patients. At baseline, total WPL was 77.0% and 72.5% in the esketamine and quetiapine cohorts, respectively. By week 8, total WPL decreased from baseline by 30.3 and 17.3 percentage points (pp) in the esketamine and quetiapine cohorts (MD = 13.0 pp; 95% confidence interval [CI], 6.3-19.8 pp), resulting in weekly cost savings of $363 and $207 (MD = $156; 95% CI, $76-$237), respectively. By week 32, total WPL decreased from baseline by 45.3 pp and 32.5 pp in the esketamine and quetiapine cohorts (MD = 12.7 pp; 95% CI, 4.7-20.7 pp), with weekly cost savings of $543 and $390 (MD = $153; 95% CI, $57-$250), respectively. Among employed adults with TRD, esketamine treatment was associated with significantly larger improvements in WPL and related costs compared to quetiapine, suggesting greater benefits from patient well-being and employer perspectives.
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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) | 0.003 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".