461-P: Diagnostic Accuracy of One Physical Examination Test vs. Three for Diabetic Peripheral Neuropathy Screening
Bibliographic record
Abstract
Introduction and Objective: ADA guidelines recommend using three physical examination tests (temperature or pinprick for small-fiber function, vibration for large-fiber function, and monofilament) for annual neuropathy screening. As some guidelines recommend choice of one test, we aimed to determine if the ADA strategy has better cross-sectional diagnostic accuracy over one test in adults with diabetes. Methods: We performed an updated analysis on baseline data from the Toronto Diabetic Neuropathy longitudinal cohort (N=478, 70 (15%) with T1D). 345(72%) participants had neuropathy according to the ‘Toronto Criteria’ nerve conduction study-based classification. Simultaneously, all underwent blinded assessment by independent examiners for pinprick, vibration (by the on-off method), and monofilament. Logistic regression and Area Under the receiver operating characteristic Curve (AUC) determined diagnostic accuracy for each testing strategy. Results: AUC for the combined three-test multiple logistic regression model was 0.84. Each of monofilament, pain, and vibration had slightly lower AUC (0.79, 0.75, and 0.74, respectively, p<0.001 for all comparisons). The optimal cut-point for the three-test model (predicted probability of 0.70) had sensitivity 80%, specificity 75%, likelihood ratio positive (LR+) 3.16, likelihood ratio negative (LR-) 0.27, and Diagnostic Odds Ratio (DOR) 11.7. The optimal cut-point for the single test with highest AUC (3 or fewer sensate monofilament responses out of 8) had sensitivity 67%, specificity 78%, LR+ 3.01, LR- 0.42, and DOR 7.2. Conclusion: The combined three-test ADA strategy had the best overall diagnostic accuracy. However, use of a single physical examination maneuver, such as the monofilament, had acceptable diagnostic performance. Future work should focus on the best strategy for prediction of future neuropathy onset. Disclosure A. Al Mulla: None. E. Parikh: None. L. Lovblom: None. A. Orszag: None. V. Bril: None. B.A. Perkins: Other Relationship; Abbott, Novo Nordisk, Sanofi. Advisory Panel; Abbott, Insulet Corporation, Sanofi, Novo Nordisk, Nephris, Vertex Pharmaceuticals Incorporated. Research Support; Novo Nordisk.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".