Transcranial magnetic stimulation for geriatric depression: Promises and pitfalls
Bibliographic record
Abstract
As the global population gets older, depression in the elderly is emerging as an important health issue. A major challenge in treating geriatric depression is the lack of robust efficacy for many treatments that are of significant benefit to depressed working age adults. Repetitive transcranial magnetic stimulation (rTMS) is a novel physical treatment approach used mostly in working age adults with depression. Many TMS trials and clinics continue to exclude the elderly from treatment citing lack of evidence in this age group. In this review, we appraise the evidence regarding the safety and efficacy of rTMS in the elderly. A consistent observation supporting a high degree of tolerability and safety among the elderly patients emerged across the Randomised Controlled Trials and the uncontrolled trials. Further, there is no reliable evidence negating the utility of rTMS in the elderly with depression. We also identified several factors other than age that moderate the observed variations in the efficacy of rTMS in the elderly. These factors include but not limited to: (1) brain atrophy; (2) intensity and number of pulses (dose-response relationship); and (3) clinical profile of patients. On the basis of the current evidence, the practice of excluding elderly patients from TMS clinics and trials cannot be supported.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".