Scaling up a success story: how do we achieve universal access to transcranial magnetic stimulation?
Bibliographic record
Abstract
PURPOSE OF REVIEW: Over one billion people suffer from psychiatric and/or neurological disorders for which transcranial magnetic stimulation (TMS) has shown efficacy. Achieving widespread TMS access will require major improvements to cost and convenience. A key figure of merit for a given TMS protocol concerns not its remission rate or tolerability, but simply its treatment time per remission (TTPR). RECENT FINDINGS: Outcomes for conventional bilateral TMS protocols imply a TTPR of more than 100 h - incompatible with widespread access. However, briefer accelerated and/or theta-burst protocols may improve TTPR to less than 20 h. Personalization strategies that improve remission rates sometime improve TTPR, depending on associated cost-penalties of time or complexity. A potentially groundbreaking new strategy to improve TTPR involves pharmacological augmentation of neuroplasticity, using agents such as D -cycloserine. Plasticity-augmentation may improve TTPR by improving remission rates, and by reducing the number of required sessions. Recent literature suggests that TTPR values of under 5 h per remission may be achievable via neuroplasticity augmentation. SUMMARY: The most recent plasticity-augmented TMS protocols may approach or exceed cost-parity with pharmacotherapy, in terms of reducing the prevalence of depression. With the health economics of TMS improving steadily, a pathway to universal access may be within reach.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".