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 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 | 0.006 | 0.009 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".