Effectiveness of applying the bioacoustic correction method in therapy of phantom pain syndrome in amputees after mine-blast injuries
Bibliographic record
Abstract
Phantom pain syndrome (PPS) has attracted the attention of scientists and physicians for many years. In recent decades, the number of patients with PPS has increased due to the growing number of victims of local conflicts, natural disasters, and industrial accidents, as well as the rise in occlusive vascular diseases of the lower extremities. Given the pathogenesis of PPS, treatment for this condition is aimed at suppressing the pathological activity of peripheral pacemakers and hyperexcitable neuronal aggregates and activating the antinociceptive system. Bioacoustic correction, an innovative technique that has demonstrated its effectiveness in the complex treatment of functional CNS disorders and the consequences of organic brain damage, is currently one of the most promising approaches to treating PPS. OBJECTIVE: To assess the severity of phantom pain syndrome in patients with amputated limbs following mine blast injury using a course of bioacoustic correction. MATERIAL AND METHODS: =30). The control group received only basic drug therapy (BMT) according to the protocol. Patients in the intervention group, in addition to BMT, received BAC procedures (10 25-minute sessions daily). The effectiveness of phantom pain syndrome therapy and concomitant psychophysiological status disorders was assessed based on the dynamics of the following parameters: neurological examination data, phantom pain syndrome severity, visual analog pain scale (VAS); DN4 questionnaire; Pain Detect questionnaire; McGill Pain Questionnaire short form; Epworth sleep quality questionnaire. RESULTS: The treatment regimen in the control and study groups demonstrated significant positive dynamics across all pain assessment scales. However, a comparative analysis of the obtained results revealed a significant advantage of additional BAC therapy. The use of this non-drug technology contributed to a significant reduction in the severity of neuropathic pain and the proportion of patients with abnormal sleepiness. CONCLUSION: The use of the BAC technique promotes a more pronounced analgesic effect, which is achieved through adaptive reorganization of thalamocortical detectors in the affected limbs by modulating neuroplasticity processes.
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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".