Effects of repetitive transcranial magnetic stimulation on cognitive function in patients with lesions in prefronal cortex
Bibliographic record
Abstract
Objective To investigate the effects of repetitive transcranial magnetic stimulation (rTMS) on cognitive function and safety in patients with lesions in prefrontal cortex (PFC). Methods Twenty-one patients with lesions in PFC hospitalized in the Department of Neurosurgery and Department of Neurology, Anqing Hospital Affiliated to Anhui Medical University between January 2012 and October 2014 were enrolled and given regular drug treatment as a background. Event-related potential (ERP) P300 latency and amplitude, Montreal Cognitive Assessment (MoCA) scores were recorded and compared before and after 4-week rTMS treatment. Results Compared with those before rTMS treatment, P300 latency ((367.38±9.79) ms vs (345.43±11.31) ms; t=5.33, P<0.05) was significantly shortened, while amplitude ((4.79±1.02) μV vs (7.84±1.40) μV; t=-8.08, P<0.05), MoCA scores (19.57±2.06 vs 23.91±1.30; t=-8.14, P<0.05), memory test scores (2.19±0.81 vs 4.10±0.89; t=-7.24, P<0.05) and executive function test scores (2.52±1.08 vs 3.57±0.93; t=-3.38, P<0.05) were obviously increased. MoCA scores (18.22±1.56 vs 20.58±1.83; t=-3.11, P<0.05), memory test scores (1.89±0.78 vs 2.42±0.79; t=-2.26, P<0.05) and executive function test scores (1.56±0.53 vs 3.25±0.75; t=-5.76, P<0.05) showed statistically significant difference before rTMS treatment in patients with lesions in either right or left front lobe. Conclusions rTMS which is thought to be a safe procedure can improve cognitive function in patients with lesions in prefrontal cortex. Key words: Transcranial magnetic stimulation; Prefrontal cortex; Cognition; Executive function; Brain damage, chronic
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".