Treating to target in major depressive disorder: response to remission to functional recovery
Bibliographic record
Abstract
Treating to target in chronic diseases [e.g. Major Depressive Disorder (MDD)] fosters precision, consistency, and appropriateness of treatment selection and sequencing. Therapeutic target definitions/endpoints in MDD should satisfy patient-, provider-, and societal expectations. Functional recovery in depression and return to both physical and mental health are the overarching therapeutic objectives. Treating to target in MDD implies multidimensional symptomatic remission, with a particular emphasis on cognitive function and aspects of positive mental health. Several atypical antipsychotic agents (i.e. brexpiprazole, aripiprazole, quetiapine) are FDA-approved as augmentation agents in MDD. Vortioxetine, duloxetine, and psychostimulants have evidence of independent, direct, and robust effects on cognitive function in MDD. Vortioxetine is the only agent that demonstrates efficacy across multiple cognitive domains in MDD associated with functional recovery. Measurement-based care, health information technology/systems, and integrated care models (e.g. medical homes) provide requisite tools and health environments for optimal health outcomes in MDD. Achieving remission in MDD does not equate to health. Return to positive mental health as well as full functioning provide the impetus to pivot away from traditional provider-defined outcomes toward an inclusive perspective involving patient- and society-defined outcomes (i.e. optimization of human capital). As in other chronic diseases, treating to target (e.g. cognitive function) further increases the probability of achieving optimal health outcomes.
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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 | 0.006 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".