Transcranial Magnetic Stimulation for Treating Psychiatric Conditions: What Have We Learned So Far?
Bibliographic record
Abstract
T he use of somatic interventions to control or treat mental symptoms dates back to ancient times.1,2 During 19171,2 During to 1937, 4 , 4 methods for producing physiological shock were discovered, tested, and used in psychiatric practice for treating psychosis: fever, insulin-induced coma, medication-induced convulsions, and electrically induced convulsions.In 1937, Cerletti and Bini 3 applied transcranial electroconvulsive shock therapy to induce seizures safely and reliably.It was received with great enthusiasm, given the remarkable therapeutic effects (in patients who now would most likely be classified as psychotically depressed) and the technical ease of administration, compared with insulin or metrazol shock.Since then, electroconvulsive therapy (ECT) has become the method of choice for convulsive therapy.4 It is currently reserved for treatment-resistant depression or special patient populations, such as the elderly.More than 60 years of experience has significantly improved this technique and made it safer to administer.However, it is associated with cognitive side effects and a high relapse rate.It is noteworthy that unlike other contemporary somatic interventions, such as transcranial magnetic stimulation (TMS), vagus nerve stimulation (VNS), or deep brain stimulation (DBS), the Food and Drug Administration (FDA) never approved ECT for clinical use as such regulations came into effect much later.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.022 | 0.014 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".