Evaluation of three screening tests and a risk assessment model for diagnosing peripheral neuropathy in the diabetes clinic
Bibliographic record
Abstract
OBJECTIVE: With the aim of evaluating predictive power, three simple screening tests as alternates to nerve conduction tests for diagnosing diabetic peripheral neuropathy (DPN) were investigated. Results of the screening tests, along with the subjects' demographic and clinical characteristics, were planned as the variables for the development of a risk assessment tool for predicting DPN. DESIGN: This is a cross‐sectional multi‐group comparison study. The study utilized a predictive model derived from one subset of the study population, and prospectively tested in the other subset to predict the presence of neuropathy. SETTING: Diabetic Neuropathy Research Clinic of the Toronto General Hospital and University Health Network in Toronto, Ontario, Canada from June 1998 to August 1999. Sample population: data come from 478 subjects consisting of non‐diabetic reference subjects, and patients with type I and type 2 diabetes mellitus. OUTCOME MEASURES: Nerve conduction studies (NCS) comprised the primary defined outcome. The three screening sensory tests examined in the study were the Semmes‐Weinstein 10 g monofilament examination (SWME), superficial pain sensation, and vibration by the on‐off method. RESULTS: The three screening tests are significantly and positively correlated with NCS. An increase in the number of insensate responses in the screening test is associated with an increase in the abnormal NCS score. The strength of the association between NCS and each sensory test was greater when the neuropathy severity stage of the subject was added to the model. Both the SWME and vibration by the on‐off method tests demonstrated sufficient statistical power to differentiate non‐diabetic control subjects from subjects with diabetes, as well as to differentiate subjects with diabetes with and without neuropathy. These two tests, when compared with NCS, also demonstrated acceptable diagnostic performance characteristics in terms of high sensitivity and specificity, total number of correctly predicted cases, and receiver‐operating characteristic curves. CONCLUSION: This data, through the development of a model involving training and validation sets, demonstrates that the knowledge of clinical risk factors alters the interpretation of sensory tests for DPN. This finding lends further support to the validity of simple sensory testing maneuvers in the conditional diagnosis of DPN. We recommend annual screening with either the SWME or vibration by the on‐off method in the primary care and diabetes clinics.
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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 | 0.025 | 0.056 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".