Noninvasive brain stimulation for the treatment of neurocognitive disorders: right for prime time?
Bibliographic record
Abstract
PURPOSE OF REVIEW: Neurocognitive disorders are associated with tremendous burden at the level of the individual, the care giver, and society at large. No effective treatments have been discovered to date. RECENT FINDINGS: Noninvasive brain stimulation (NIBS) comprises several promising interventions that have been studied in Alzheimer's disease and related dementias. Most recent studies have tested transcranial direct current stimulation or repetitive transcranial magnetic stimulation on their own or in combination with other interventions, particularly cognitive training. While most studies were proof-of-principle studies with small sample sizes, combination and long-duration protocols seem to be promising approaches to pursue. Some studies also investigated novel neurophysiological markers as predictors of response to NIBS. SUMMARY: NIBS presents several interventional options that are ready to be evaluated using well powered, long-duration trials. These future studies should build on the promising leads from the current literature, including the potential advantage of combining NIBS with other interventions; the delivery of interventions for long durations to assess long-term impact; and the use of neurophysiological markers that could optimize the personalization and efficacy of NIBS.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".