NerveCheck for the Detection of Sensory Loss and Neuropathic Pain in Diabetes
Bibliographic record
Abstract
BACKGROUND: Accurate and economic detection of nerve damage in diabetes is key to more widespread diagnosis of patients with diabetic peripheral neuropathy (DPN) and painful diabetic neuropathy. This study examined the diagnostic performance of NerveCheck, an inexpensive ($500) quantitative sensory testing (QST) device. METHODS: One hundred forty-four subjects (74 with and 70 without diabetes) underwent assessment with NerveCheck, neuropathy disability score (NDS), nerve conduction studies (NCS), intraepidermal and corneal nerve fiber density (IENFD and CNFD), and McGill questionnaire for neuropathic pain. RESULTS: Of the 74 subjects with diabetes, 41 were diagnosed with DPN based on the NDS. The NerveCheck scores for vibration perception threshold (VPT), cold perception threshold (CPT), and warm perception threshold (WPT) were significantly lower (P ≤ 0.0001) in diabetic patients with DPN compared to patients without DPN. The diagnostic accuracy of VPT was high with reference to NCS (area under the curve [AUC]: 82%-84%) and moderate for IENFD, CNFD, and neuropathic pain (AUC: 60%-76%). The diagnostic accuracy of CPT and WPT was moderate with reference to NCS, IENFD, and CNFD (AUC: 69%-78%) and low for neuropathic pain (AUC: 63%-65%). CONCLUSIONS: NerveCheck is a low-cost QST device with good diagnostic utility for identifying sensory deficits, comparable to established tests of large and small fiber neuropathy and for the severity of neuropathic pain.
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 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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.004 | 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".