Management of Children with Speech Disorders via Transcranial Magnetic Stimulation: Non-Randomized Controlled Study
Bibliographic record
Abstract
Background. Speech development impairment is urgent and common problem in pediatric neurology. Transcranial magnetic stimulation (TMS) is one of the promising treatment variants for children with speech disorders. Objective. The aim of the study is to evaluate efficacy and safety of the developed approaches to TMS usage in the management of children with speech disorders. Methods. It was non-randomized controlled study. It included 46 children with speech disorders aged from 3 to 6.5 years. All children were divided into two groups comparable by gender and age: 26 children of the treatment group received TMS course, 20 children of the control group received treatment with hopantenic acid. All patients with speech disorders underwent psychological and pedagogical evaluation of speech and cognitive development, electroencephalography (EEG) before and after treatment. Moreover, comparative analysis of TMS and nootropic therapy efficacy was carried out. Specialized examination of speech and cognitive development was also performed via E.A. Strebeleva method for psychological and pedagogical diagnosis of children development. Furthermore, we carried out side reactions / adverse events registration according to patients and/or their parents complaints confirmed by physical examination, patient’s behavior observation, data from specially developed questionnaire for assessing child’s behavior and well-being (filled up by parents). Finally, we evaluated brain bioelectric activity recorded by EEG. Results. The study results have shown that it is possible to achieve significant positive dynamics in cognitive and speech development in preschool children with speech disorders in both groups (TMS course and medical treatment). But hereby, TMS treatment has demonstrated significantly higher positive dynamics in two out of the three evaluated parameters. There were no cases of adverse events in TMS group leading to early course discontinuation. Conclusion. TMS is non-invasive and safe method for treatment of children with speech disorders. This study has demonstrated the efficacy of the method in the field of personalized management of children with impaired speech and cognitive development.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".