A STUDY OF THE PATTERN OF RESPONSE TO rTMS TREATMENT IN DEPRESSION
Bibliographic record
Abstract
BACKGROUND: Considerable research has demonstrated the efficacy of repetitive transcranial magnetic stimulation (rTMS) treatment in patients with depression. However, limited research has described the pattern of response to rTMS treatment or explored possible predictors of the likelihood of treatment response. METHODS: Data from 11 clinical trials (n = 1,132) was pooled and we described the pattern of response to rTMS, rate of response, and remission as well as potential clinical and demographic predictors of response. RESULTS: There was a bimodal pattern of response to rTMS with the response-associated peak at 57% reduction in depression rating scale scores. About 46% of patients achieved response criteria, with 31% completing rTMS treatment in remission. A greater likelihood of response was seen for patients who had less severe depression at baseline, a shorter duration of the current episode, and recurrent rather than single episode of depression. Greater response was also seen in patients treated at higher stimulation intensity. CONCLUSIONS: A meaningful percentage (>40%) of patients respond to a course of rTMS treatment. Response does vary with a number of clinical and demographic variables but none of these variables exert a sufficiently strong influence on response rates to warrant using these criteria to exclude patients from treatment.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".