Comparative Effectiveness Research Trial for Antidepressant Incomplete and Nonresponders With Treatment Resistant Depression (ASCERTAIN-TRD)
Bibliographic record
Abstract
This study compared the effects of augmenting antidepressants with aripiprazole or repetitive transcranial magnetic stimulation (rTMS) versus switching to venlafaxine XR/duloxetine on quality of life (QoL) among patients with treatment resistant depression (TRD). In a predefined secondary analysis of a multisite, open-label, effectiveness trial, patients with TRD were randomly assigned to aripiprazole augmentation, rTMS augmentation, or switching to venlafaxine XR/duloxetine in a 1:1:1 ratio, and they were treated for 8 weeks. TRD was defined as an inadequate response to 2 or more antidepressant trials of adequate dose and duration, as defined by the Massachusetts General Hospital Antidepressant Treatment Response Questionnaire. QoL was predefined as a key secondary end point for this study and assessed using the short form of the Quality of Life Enjoyment and Satisfaction Questionnaire (Q-LES-Q-SF). A mixed-effects model with repeated measures was applied. This study was conducted from July 13, 2017, to December 22, 2021. =.326). At end point, changes from baseline in the Q-LES-Q-SF scores were 10.61 (SE=1.0) for aripiprazole augmentation, 11.59 (SE=1.1) for rTMS augmentation, and 8.68 (SE=0.9) for venlafaxine XR/duloxetine switch. Augmentation with aripiprazole, but not rTMS, improved QoL significantly versus venlafaxine XR/duloxetine switch in TRD patients. However, a much smaller than expected sample size for the rTMS group may explain the lack of statistical significance rendering the latter finding of indeterminate nature. ClinicalTrials.gov identifier: NCT02977299.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".