Transcranial pulse stimulation (TPS) as a method for treating the central nervous system of patients with Alzheimer’s disease
Bibliographic record
Abstract
Abstract Background Dementia ‐ one of the most common diseases in old age ‐ is often only diagnosed at a late stage. Therefore patients with dementia have often a 1.4 to 3.6 times greater risk of treatment as an inpatient. Consequently it is highly relevant within the caring system to identify and treat the onset of dementia at the earliest possible opportunity. Part of a new treatment center, a psychiatric clinic in the Hanover area (Wahrendorff) has concentrated on treating patients with a mild or moderate form of Alzheimer’s disease as early as possible on an outpatient basis. The method of transcranial pulse stimulation (TPS®) with the Neurolith® system is used. Acoustic pulses generated outside the body are introduced specifically into the brain regions requiring treatment. The aim being the release of growth factors and an improvement in cerebral blood flow, as a means of maintaining and promoting cognitive performance for as long as possible. The poster contribution shows reports from clinicians, patients and relatives, using TPS®. The development of cognitive performance in the course of treatment is also considered. Method The data collection for the quantitative study design will take place at the clinic in the period from 06/2021 to 10/2022 (N = 61). Cognitive performance is recorded using the Montreal Cognitive Assessment (MoCA test) and the experience reports via interview. Result Results of repeated measurement and analysis of the variance in terms of cognitive performance (MoCA test, baseline and follow‐up measures) showed cognitive improvements. Conclusion Transcranial pulse stimulation is a new and promising approach in the treatment of Azheimer’s disease.
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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 | 0.001 |
| 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) | 0.002 | 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".