Efficacy of Transcranial Magnetic Stimulation [TMS] among Major Depressive Disorder Patients: A Review
Bibliographic record
Abstract
The objective of the review is to ascertain the efficacy of transcranial magnetic stimulation therapy among major depressive patients. The various research papers reviewed to know the magnitude of depression and the efficacy of TMS. Depression is a significant contributor to the global burden of disease and affects people in all communities across the world. India has the highest rate of major depression in the world. It is estimated that by the year 2020 the burden of depression will increase to 5.7% of the total burden of disease. It clearly depicts that the need for alternative and convenient treatment modality for depression as a whole. TMS proves to be one of the effective mode of treatment for depression. In terms of its convenience of treatment it's quite promising and lesser number and severity of side effects as compared to other mode of treatment. Currently, it's under practice in countries like Canada, UK, and Australia. In India it needs to be equipped in terms of training and education to the experts, very importantly creating awareness among patients and family about the treatment facility. TMS is hope for India and it necessitates rapid paradigm shift in order to curb the title of high prevalent country with regards to depression.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 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) | 0.003 | 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".