An examination of symptoms, function and quality of life as conjoint clinical outcome domains for treatment-resistant depression
Bibliographic record
Abstract
Objective The treatment of depression aims to improve depressive symptoms, daily function and quality of life (QoL). These exploratory analyses of a database of convenience from the RECOVER trial evaluated how a "tripartite" metric based on these 3 outcome domains might perform in treatment-resistant depression (TRD). Methods Outcome domains included depressive symptoms (MADRS, QIDS-C, QIDS-SR), function (WPAI item-6) and QoL (Mini-Q-LES-Q) obtained at months 3, 6, 9 and 12 in outpatients with markedly TRD in the double-blind RECOVER trial that compared sham to active adjunctive vagus nerve stimulation (VNS). For each domain, clinically meaningful differences were defined a priori . Analyses addressed 3 questions: 1) Does each domain detect meaningful benefits undetected by the other domains? 2) Is the tripartite metric validated by an independent clinician-rated global index of improvement (CGI-I)? 3) How well does the tripartite metric detect treatment group differences in the RECOVER trial? Results Clinically meaningful reductions in depressive symptoms alone missed 25–51 % of all participants who evidenced clinically meaningful benefits in depressive symptoms, function or QoL. The tripartite metric strongly correlated with CGI-I ratings (tetrachoric r = 0.73–0.85), with stronger relationships than each component individually. The tripartite metric successfully separated active from sham adjunctive VNS in a 1-year trial of patients with markedly TRD. Conclusion Symptoms, function and QoL capture distinct, clinically significant and valid outcomes that identify persons with markedly TRD with a range of clinically meaningful benefits. Whether these results pertain to other major depressive disorder patient groups and treatments deserves 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 | 0.014 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".