The Combination of Proteolytic Enzyme Supplementation, Acupuncture, and Osseous Manipulation for the Treatment of Traumatic Peripheral Nerve Injury: A Case Report
Bibliographic record
Abstract
This report describes the treatment of peripheral nerve injury in a 43-year-old woman using a combination of proteolytic enzymes, acupuncture, and osseous manipulation as alternatives to conventional care. Her presenting symptoms include complete loss of sensation in the superolateral left thigh and a mass of fibrotic scar tissue in the area of injury. A holistic treatment plan was created to address the underlying mechanisms of the injury. Plant-based proteolytic enzymes, bromelain and papain, were used to address tissue inflammation and reduce scar tissue formation around the nerve, acupuncture was used to regenerate the superficial nerves, and osseous manipulations were provided for structural re-alignment. After 8 weeks of treatment, the patient reported significant reduction in the size of the fibrotic mass and complete resolution of sensory loss. The fibrotic mass of tissue reduced from 6 cm to 1 cm during the treatment course. Proteolytic enzymes have wide-ranging indications; however, they have not been well studied for peripheral nerve injury, which makes this a novel indication for this natural health product. This case report found the use of proteolytic enzymes, and acupuncture, effective in treating peripheral nerve injury and provides grounds for research to treat nerve-related injuries.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.007 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".