Predicting Remission in Late-Life Major Depression
Bibliographic record
Abstract
OBJECTIVE: To determine the likelihood of antidepressant response in older adults with major depression as a function of their prior antidepressant trials. METHODS: 500 older adults with major depression as diagnosed by DSM-IV criteria for major depressive episode were treated with venlafaxine extended release for 12 weeks. Participants were recruited from July 2009 to January 2014. For each participant, we collected detailed data on prior antidepressant trials for the current episode of depression. We examined the prospective remission rates as a function of number and class of prior antidepressant trials in a post hoc analysis of pooled data from 2 prior trials. RESULTS: Remission rates with venlafaxine were inversely correlated with the number of prior adequate medication trials (66% for no prior adequate trials, 45% for 1 prior adequate trial, 23% for 2 or more prior adequate trials; P < .0001). Additionally, if prior treatment trials included a serotonin-norepinephrine reuptake inhibitor, participants were even less likely to achieve remission with venlafaxine (32% for 1 prior adequate trial, 18% for 2 or more prior adequate trials; P < .0001). Those with prior adequate trials were also more likely to require a higher dosage of venlafaxine to achieve remission. CONCLUSIONS: Information on an individual patient's number and class of prior adequate antidepressant trials can be used to predict the likelihood of a successful treatment outcome with a given antidepressant in older adults with major depression. Further work is needed to refine this approach to provide personalized antidepressant treatment. TRIAL REGISTRATION: ClinicalTrials.gov identifiers: NCT00892047 and NCT02263248.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".