Transcranial magnetic stimulation promotes cognition among epileptics after traumatic brain injury
Bibliographic record
Abstract
Objective To observe the effect of low-frequency, repetitive transcranial magnetic stimulation (rTMS) at different frequencies on the cognitive function of traumatic brain injury patients with a history of epilepsy. Methods Sixty traumatic brain injury patients were randomly divided into a 0.5 Hz group, a 1.0 Hz group and a control group, each of 20. In addition to routine drug therapy and cognition training, the control group was given fake stimulation, the 0.5 Hz group was treated with 0.5 Hz rTMS, and the 1.0 Hz group was provided with 1.0 Hz rTMS for 4 weeks, eleven times per week. Before and after treatment, the cognitive function of all three groups was assessed using the Montreal cognitive assessment (MOCA), the Rivermead behavior memory test (RBMT) and a symbol cancellation test. The number of patients reporting headache or epilepsy during the treatment period was also counted. Results During the treatment, there was no headache case in any of the groups, and no significant difference was found in the occurrence of seizures. After the treatment, all of the measurements in all 3 groups had improved significantly. The average MOCA and RBMT scores in the 1.0 Hz group were all significantly better than those in the control group, but there was no significant difference between the 0.5 Hz group and the control group. The symbol cancellation test efficiency of the 1.0 Hz group was not significantly better than that of the 0.5 Hz and control groups. Conclusions Repeated 1.0 Hz transcranial magnetic stimulation can significantly improve cognition after traumatic brain injury among patients with a history of epilepsy without increasing the risk of seizures. Key words: Trauma; Brain injury; Transcranial magnetic stimulation; Cognition; Epilepsy
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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".