477-P: Peripheral Neuropathy—Evaluation of DPNCheck vs. mTCNS in Type 1 Diabetes
Bibliographic record
Abstract
Introduction: The handheld DPNCheck (DPNC) nerve conduction study (NCS) device has shown high sensitivity (SENS) and specificity (SPEC) against classical NCS for detecting peripheral neuropathy (PN), but studies comparing DPNC to clinical assessments are lacking. We aimed to evaluate DPNC against a validated clinical reference standard in adults with T1D. Methods: Among 88 adults >50 yrs with T1D, we assessed PN with the modified Toronto Clinical Neuropathy Scale (mTCNS) and bilateral sural nerve conduction velocity (VEL) and action potential amplitude (AMP) with DPNC. Abnormal AMP or VEL >1 leg=DPNC+. We used McNemar’s test and calculated SENS and SPEC to compare DPNC to mTCNS (score >3=PN+) as the reference. Results: Mean age was 63+6 yrs, T1D duration 46±10 yrs and A1c 7.1±1%; 52% were male. Mean mTCNS score was 5.2+5, AMP 42+10 m/s and VEL 4.8+3 µV. Correlation between mTCNS and worse leg AMP and VEL was -0.36 and -0.52, respectively (p<0.001). The proportion of subjects PN+ by mTCNS was similar to those DPNC+ using standard manufacturer cutoffs (DPNC-std; p=0.85) but significantly higher in those DPNC+ using age and height adjusted cutoffs (DPNC-adj; p<0.001). DPNC-adj had 94% SENS but 28% SPEC; DPNC-std had 71% SENS and 63% SPEC. Conclusion: DPNC has high SENS for detecting PN in T1D. Lower SPEC may reflect detection of subclinical PN rather than misclassification of true negatives. DPNC alone or combined with mTCNS may aid in early PN detection. Disclosure L.A. Marion: None. C.E. Nevarez: None. A.B. Murthy: None. E.W. Yu: Research Support; Amgen Inc. Funding National Institutes of Health (T32DK007028)
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".