Feasibility and effectiveness of electrochemical dermal conductance measurement for the screening of diabetic neuropathy in primary care. DECODING Study (Dermal Electrochemical Conductance in Diabetic Neuropathy). Rationale and design
Bibliographic record
Abstract
Diabetes mellitus is the leading cause of polyneuropathy in the Western world. Diabetic neuropathy is a frequent complication of diabetes and may have great clinical transcendence due to pain and possible ulceration of the lower extremities. It is also a relevant cause of morbidity and mortality in patients with diabetes. Although the cause of polyneuropathy in patients with diabetes is only partially known, it has been associated with chronic hyperglycemia suggesting the possible etiopathogenic implication of advanced glycosylation end products. The strategy of choice in the medical management of diabetic neuropathy is early detection since glycaemic control and the use of certain drugs may prevent or slow the development of this disease. Diabetic neuropathy most often presents with a dysfunction of unmyelinated C-fibers, manifested as an alteration of the sweat reflex of the eccrine glands. This dysfunction can now be demonstrated using a newly developed technology which measures dermal electrochemical conductivity. This noninvasive test is easy and cost-effective. The aim of the present study is to evaluate the feasibility and effectiveness of dermal electrochemical conductance measurement (quantitative expression of the sudomotor reflex) as a screening test for the diagnosis of diabetic neuropathy in patients in primary care.
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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 | 0.005 | 0.009 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".