Trajectories of response to bilateral rTMS in late-life depression
Bibliographic record
Abstract
BACKGROUND: Late-life depression is often resistant to standard treatment (LL-TRD) and presents unique clinical challenges due to comorbidities and cognitive decline. Repetitive transcranial magnetic stimulation (rTMS) is a promising option, yet responses are variable. Identifying trajectories of symptom change in LL-TRD in response to rTMS may clarify this heterogeneity and guide more personalized interventions. METHODS: This secondary analysis of a randomized rTMS trial in late-life depression used group-based trajectory modeling to identify depressive symptom response patterns. 172 participants aged 60+ were randomly assigned to one of two protocols: (1) bilateral rTMS, with low-frequency stimulation applied to the right dorsolateral prefrontal cortex (DLPFC) and high-frequency stimulation to the left; or (2) bilateral theta burst stimulation, with continuous TBS on the right DLPFC and intermittent TBS on the left. Multinomial regression identified baseline characteristics associated with trajectory membership. RESULTS: Four symptom trajectories were identified: Nonresponse, Partial Response, Linear Response and Rapid Response. Relative to Partial Response, higher Montgomery-Åsberg Depression Rating Scale (MADRS) scores were associated with lower odds of Rapid (OR = 0.79, 95 %CI:0.69-0.90) and Linear Response (OR = 0.87, 95 %CI:0.78-0.97), and higher odds of Nonresponse (OR = 1.33, 95 %CI:1.16-1.52). Benzodiazepine use was associated with lower odds of Linear Response (OR = 0.22, 95 %CI:0.08-0.56), while higher baseline anxiety was associated with higher odds of Nonresponse (OR = 1.13, 95 %CI:1.01-1.26). CONCLUSION: This study identified four distinct rTMS response trajectories in LL-TRD and found that greater baseline depression severity and anxiety were associated with worse trajectories. These results support early clinical profiling to identify individuals at risk for nonresponse. CLINICALTRIALS: gov identifier NCT02998580.
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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 | 0.004 |
| 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".