Influence of high-velocity low amplitude manipulation of the lumbar spine on the activity of motor units of the erector spinal muscles with functional assessment in healthy young people
Bibliographic record
Abstract
Background: In recent years, many researchers have focused on the problem of patients who have multiple sclerosis.This disease affects more and more younger people who suffer from paresis, balance disorders, and weakening of muscle strength and coordination, which affects the deterioration of gait.The Vojta method ideally improves the quality of movement of people suffering from neurological diseases.Numerous studies prove this. Aims:The case study aimed to analyze the effect of Vojta stimulation on the change of gait parameters in a man with multiple sclerosis. Case report:The case of a 56-year-old man who has multiple sclerosis for 7 years (height 178 cm, weight 88 kg, BMI 27.7) is presented.The stage of disease development is rated 5 on the EDSS scale.The patient underwent Vojta therapy 3 times a week for 3 weeks (9 treatments).A single treatment lasted 30 minutes and included reflex crawling therapies.Gait parameters were measured using functional tests prior to the study.After completing the series of treatments, an improvement in gait parameters and better balance maintenance were found. Summary:In the case of a man with multiple sclerosis for 7 years, the use of the Vojta method in gait therapy improved his parameters.Nevertheless, further clinical trials should be conducted, also based on measuring parameters based on measuring equipment on a larger research group.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".