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Record W4402633173 · doi:10.54434/candj.176

The Combination of Proteolytic Enzyme Supplementation, Acupuncture, and Osseous Manipulation for the Treatment of Traumatic Peripheral Nerve Injury: A Case Report

2024· article· en· W4402633173 on OpenAlexaffvenue
Tamara Clarke, Sherry Wilson

Bibliographic record

VenueCAND Journal · 2024
Typearticle
Languageen
FieldMedicine
TopicAcupuncture Treatment Research Studies
Canadian institutionsBoucher Institute of Naturopathic Medicine
Fundersnot available
KeywordsProteolytic enzymesAcupunctureMedicinePeripheral nervePeripheralPeripheral nerve injuryTraumatic injuryNerve injuryEnzymeSurgeryPathologyInternal medicineBiochemistryAnatomyChemistryAlternative medicine

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.700
Threshold uncertainty score0.232

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.042
GPT teacher head0.370
Teacher spread0.328 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes2
Has abstractyes

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