Comparison of conventional and non-invasive techniques for the early identification of diabetic neuropathy in children and adolescents with type 1 diabetes
Bibliographic record
Abstract
BACKGROUND: Neuropathy is an important complication and contributes to the morbidity of diabetes mellitus. The availability of simple and non-invasive tests for screening of early diabetic neuropathy (DN) in children with diabetes may prevent further progression of this complication. The purpose of this study was to compare conventional nerve conduction studies (NCS) with non-invasive techniques, including vibration perception thresholds (VPT) and tactile perception thresholds (TPT) for the detection of DN in children and adolescents with type 1 diabetes. METHODS: Children from the Alberta Children's Hospital Diabetes Clinic with at least 5 yr duration of type 1 diabetes underwent detailed evaluations, including neurologic exam, NCS, VPT, and TPT testing. Information on duration of diabetes, height, and mean glycosylated hemoglobin (A1C) were also collected. Descriptive statistics, including Student's t-test and chi-squared test, were used for analysis. RESULTS: Seventy-three children (mean age of 13.7+/-2.6 yr) completed the study. The mean duration of diabetes was 8.1+/-2.6 yr, and the mean A1C was 9.0+/-1.0%. Forty-two (57%) children had DN based on NCS. Using NCS as a gold standard, the sensitivity and specificity of VPT were 62 and 65%, while the sensitivity and specificity of TPT were 19 and 64%, respectively. CONCLUSIONS: Subclinical DN is common among children and adolescents with type 1 diabetes, and there is a need for better metabolic control in this population. VPT and TPT may not be adequate screening tools for the detection of DN in children.
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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 | 0.003 | 0.008 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".