Repetitive Transcranial Magnetic Stimulation for Treatment of Medication-Resistant Depression in Older Adults
Bibliographic record
Abstract
UNLABELLED: The antidepressant effects of repetitive transcranial magnetic stimulation (rTMS) are well documented, but studies to date have produced heterogeneous results in late-life depression. OBJECTIVE: To address this matter, we evaluated the efficacy of both high- and low-frequency rTMS delivered to the prefrontal cortex of older adults with treatment-resistant major depression. METHODS: Forty-nine older adults (69 +/- 6.7 years) with treatment-refractory major depressive disorders underwent a series of rTMS treatments as an adjuvant to pharmacotherapy. Patients received high-frequency rTMS delivered to the left dorsolateral prefrontal cortex, low-frequency stimulation to the right dorsolateral prefrontal cortex, or a combination thereof, at 80-110% of the motor threshold. RESULTS: There was a modest, but statistically significant, mean reduction (24.7%) in Hamilton Depression Rating Scale (HDRS) scores from baseline to the end of treatment. Nine patients were classified as responders (50% HDRS reduction), and 4 patients reached remission status (final HDRS score <8). Similar improvements in HDRS scores were observed for high- and low-frequency rTMS. Treatment was generally well tolerated, and no serious adverse effects were reported. CONCLUSIONS: The findings support the contention that in older adults with treatment-refractory depression, rTMS can be an effective treatment alternative for some patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".