Predictors of remission after repetitive transcranial magnetic stimulation for the treatment of late-life depression
Bibliographic record
Abstract
Repetitive transcranial magnetic stimulation (rTMS) is an effective treatment in patients with depression, yet treatment response remains variable. While previous work has identified predictors of remission in younger adults, relatively little data exists in late-life depression (LLD). To address this gap, data from 164 participants with LLD from a randomized non-inferiority treatment trial comparing standard bilateral rTMS to bilateral theta burst stimulation (TBS) (ClinicalTrials.gov identifier: NCT02998580) were analyzed using binary logistic regression and conditional inference tree (CIT) modeling. Lower baseline depression symptom severity, fewer prior antidepressant treatment failures, and higher global cognition predicted remission following rTMS treatment. The CIT predicted a higher likelihood of achieving remission for patients with a total score of 19 or lower on the Montgomery-Åsberg Depression Rating Scale, 1 or fewer prior antidepressant treatment failures, and a total score of 23 or higher on the Montreal Cognitive Assessment. Our results indicate that older adults with lower severity of depression, fewer antidepressant treatment failures, and higher global cognition benefit more from current forms of rTMS. The results suggest that there is potentially higher value in using rTMS earlier in the treatment pathway for depression in older adults.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".