THE EFFECT OF NEUROSTIMULATION ON ISCHEMIC PAIN AND METHODS OF ASSESSING PAIN
Bibliographic record
Abstract
Peripheral arterial disease (PAD) impacts approximately eight million people in the United States [1]. Disease progression leads to chronic ischemic pain, hindering quality of life. Pharmaceuticals are a typical treatment for pain associated with PAD; but as few as 30% of patients have a significant reduction of pain (≥50%) [2]. Neurostimulation is commonly used as a treatment for various diseases and injuries, including Parkinson’s disease and sports-related back and knee injuries [2]. The objective of the study was to explore neurostimulation and its effect on pain and paresthesia for a model of acute peripheral ischemia in young college students. Pain is highly subjective and as a result can be difficult to measure. As a result, various pain scales and questionnaires exist and are commonly used for self-reported measurement of pain. Based on literature and prior pilot work, three instruments for measuring pain were employed to determine which would provide the best signal to noise ratio. Of all the instruments tested, the McGill Pain questionnaire best showed differences in pain in this study, with the best signal to noise ratio, and is recommended for future research and clinical assessment of ischemic pain. Neurostimulation treatment did not cause a statistically significant reduction in pain. However, different trends are seen among different patients with some patients having an apparent decrease in pain with transcutaneous electrical nerve stimulation (TENS) treatment while others have an apparent decrease in pain with interferential currents stimulation (IFC) treatment. This indicates that it would be worthwhile to further explore neurostimulation and determine what causes the differing responses. Based on the differing responses, neurostimulation should be pursued as a method of ischemic pain reduction that could be tailored to the specific patient based on what neurostimulation best helps them.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".