Bibliographic record
Abstract
The use of non-invasive brain stimulation (NIBS) techniques to study the brain has increased significantly in recent decades. It has become one of the most accepted therapeutic approaches and powerful tools in treating neurological and psychiatric disorders. NIBS, such as Transcranial magnetic stimulation (TMS) and transcranial direct current stimulation (tDCS), have been proven effective in several clinical conditions, such as major depressive disorders, stroke, and to improve addition/craving and cognition, in both young and geriatric populations (Yavari et al., 2016; Lefaucheur et al., 2020; Fregni et al., 2021; Teixeira-Santos et al., 2022). However, methods and protocols of brain stimulation are very heterogeneous and further research is needed to fine tune the modulatory effects of NIBS in the brain. In this research topic of methods and protocols of brain stimulation, five original research articles address various protocols of NIBS, such a study protocol for geriatric depression, a perspective article on how to test the association between baseline performance and effects of NIBS, and clinical trials discussing methods of brain stimulation on stroke and sleep quality.
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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 | 0.008 | 0.033 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.013 | 0.018 |
| Insufficient payload (model declined to judge) | 0.023 | 0.028 |
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".