Impact of repetitive transcranial magnetic stimulation with computerized cognitive enhancement training on change in cognitive function of mild cognitive impairment patients: Existing, sham treatment‐controlled, pilot study
Bibliographic record
Abstract
Abstract Background This study aimed to investigate the effects of repetitive transcranial magnetic stimulation (rTMS) and computerized cognitive enhancement training combined treatment on cognitive function in mild cognitive impairment (MCI) patients, comparing to the application of the rTMS alone and the sham stimulation. Method Six MCI patients received high‐frequency rTMS applied to the bilateral dorsolateral prefrontal cortex, followed by the computerized cognitive enhancement training. In addition, six MCI patients took high‐frequency rTMS alone. A sham stimulation was used as a control condition in 7 MCI patients. All stimuli were delivered at a frequency of two sessions per week for four weeks. Neuropsychological test was performed at each time points in each group. Result Although there was the main effect of interaction between time and group on the score of the Korean version of Montreal Cognitive Assessment, only participants of the rTMS single treatment group showed significant improvement of this score. Conclusion In order to overcome the weaknesses of previous studies confirming the efficacy of the combination therapy, this study included patients with MCI, which was the pre‐dementia stage, and included rTMS single treatment group, but did not confirm the clinical efficacy of the combination therapy. Small sample size, the baseline cognitive function score, and the ceiling effect of the group were considered to be the bias factor of this study and further studies supplementing these factors are needed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 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".