Usefulness of the NC-stat DPNCheck nerve conduction test in a community pharmacy as an educational tool for patients with diabetes
Bibliographic record
Abstract
Diabetic peripheral neuropathy (DPN) is a disabling long-term microvascular complication of diabetes mellitus. It is estimated that 40% to 50% of people with diabetes will have detectable DPN within 10 years of diagnosis.1 Patients with diabetes with peripheral neuropathy are at an increased risk for foot ulceration that, if untreated, could result in amputation and neuropathic pain that can cause significant morbidity. Chronic sensorimotor DPN is the most commonly seen neuropathy in diabetes. Patients may experience symptoms such as burning pain, electrical or stabbing sensations, paresthesia, hyperesthesia and deep aching pain.2 The Canadian Diabetes Association Clinical Practice Guidelines (CDA CPG) 2013 recommends that people with type 2 diabetes should be screened for DPN at diagnosis and then annually thereafter.1 Patients with type 1 diabetes should be screened 5 years after the postpubertal duration of diabetes and then annually thereafter.1 Screening for DPN can be conducted using a 10 g Semmes-Weinstein monofilament or a 128-Hz tuning fork.1 The early detection and control of DPN are crucial because up to 50% of patients may be asymptomatic.2 This puts patients at risk for developing unnoticed injuries to their feet, leading to foot ulcers.2 A large number of patients with DPN are not identified, and they are likely to miss early intervention to prevent the progression of DPN.3 NC-stat DPNCheck, manufactured by NeuroMetrix Inc. (Waltham, MA), is a point-of-care device that measures sural nerve conduction velocity (CV) and sensory nerve action potential (SNAP) amplitude. Sensory nerve action potential amplitude and CV have been shown to be sensitive indicators of nerve degeneration in patients with diabetes and have been used to detect DPN.4 Diabetic peripheral neuropathy is associated with low SNAP amplitude and CV.4 This instrument has been shown to have a sensitivity of 92% and a specificity of 82% when compared to traditional nerve conduction studies in patients with DPN, with reproducible results.5,6 This article reports the use of the NC-stat DPNCheck testing device in the community pharmacy setting as an assessment tool for pharmacists when educating patients regarding DPN and glycemic control.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".