Comparing the Diagnostic Accuracy of Simple Tests to Screen for Diabetic Peripheral Neuropathy: Protocol for a Cross-Sectional Study
Bibliographic record
Abstract
BACKGROUND: Various tests are used to detect diabetic peripheral neuropathy by assessing sense perception in the feet. Tests vary in terms of time and resources required. Simple tests are those that can be conducted quickly and easily in primary care without laboratory equipment. There are some limitations to these simple tests, an example being the variable amplitude of the 128 Hz tuning fork. A new test, VibraTip (McCallan Medical, UK), might be a valuable alternative as it emits a consistent amplitude and may offer improved diagnostic accuracy. OBJECTIVE: The aims of this study are to estimate the diagnostic accuracy of the VibraTip device for diabetic peripheral neuropathy against the reference standard of sural nerve conduction velocity measurement, and to assess whether the VibraTip offers superior diagnostic accuracy to other routine tests based on vibration or touch. METHODS: The study will prospectively recruit adults with type 2 diabetes who are due to attend a routine follow-up clinic. A cross-sectional study design will be employed to assess the diagnostic accuracy of 5 standard index tests for peripheral neuropathy, including VibraTip. The reference test will be sural nerve conduction velocity measurement. RESULTS: Funding is being sought to conduct this research. The outcomes assessed will be the diagnostic accuracy of the 5 index tests against sural nerve conduction velocity measurement, including sensitivity, specificity, positive predictive value, negative predictive value, positive likelihood ratio, and negative likelihood ratio. Receiver operating characteristic curves will be constructed and compared for each test. CONCLUSIONS: This study will be the first within-study comparison of 5 simple tests for screening diabetic peripheral neuropathy and will address uncertainties in the potential benefits of using VibraTip in comparison with the other tests.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".