154-OR: Combined Point-of-Care Devices, DPN-Check and Sudoscan, Diagnose Early DPN and Predict Diabetic Foot Ulceration
Bibliographic record
Abstract
Introduction and Objective: Diabetic peripheral neuropathy (DPN) is the strongest risk factor for diabetic foot ulceration (DFU). Current screening methods (e.g. 10g monofilament [10g-MFT]) are crude and diagnose DPN late when the condition is already well established. Point-of-care devices (POCDs), DPN-Check, a hand-held device that measures nerve conduction/amplitude and SUDOSCAN that measures sudomotor function, diagnose DPN early. However, their abilities to predict incident DFU is unknown. Methods: 229 consecutive participants with diabetes were followed up for 9 years and underwent detailed assessments including: the Toronto Clinical Neuropathy Score (TCNS), 10g-MFT, and POCD. Risk of incident DFU was analysed using odds-ratios for participants with abnormal baseline TCNS, 10gMFT and combined POCDs tests who developed DFU at follow-up. Results: At baseline, the prevalence of DPN diagnosed was 14.4%, 27% and 66.8% for MFT(n=33), TCNS(n=62) and POCD(n=153) respectively. 100 participants attended the 9 year follow-up however 59 participants died. Those who attended follow up visits were significantly younger [58.0(11.1)vs59.1(17.8) years;(p<0.001(95%CI:4.1:11.4)], had fewer co-morbidities [2.7vs3.2; P<0.001] and lower Q-risk score (21.4(12.9)vs24.5(14.4); p<0.001). Thirty-five (15.3%) participants developed DFU. Participants with abnormal POCDs at baseline demonstrated a 4.4-fold increased risk [(n=31),95%CI:1.5:12.9, p<0.004)] of developing DFU compared to those with normal POCDs at baseline. There was also an increased risk of incident DFU with DPN diagnosed using TCNS [(n=17), OR 2.9(95%CI:1.4:6.1, p=0.004)] and 10g-MFT [(n=13), OR 4.5(95%CI:1.9:10.4, p<0.001)]. Conclusion: We have demonstrated that abnormal POCD tests predict greater number of incident DFUs. These results advocate the use of POCDs to predict DFUs at an early stage, when preventative measures can be implemented. Disclosure M. Goonoo: None. D. Selvarajah: None. G.P. Sloan: Speaker's Bureau; Eli Lilly and Company, Procter & Gamble. S. Tesfaye: Advisory Panel; AstraZeneca, Bayer Pharmaceuticals, Inc. Speaker's Bureau; Berlin-Chemie AG. Advisory Panel; Grünenthal. Speaker's Bureau; Metronics, Novo Nordisk. Advisory Panel; Procter & Gamble. Speaker's Bureau; Viatris Inc. Advisory Panel; Nevro Corp. Funding Proctor and Gamble (STH22196)
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.021 | 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".