Cooling Detection Thresholds in the Assessment of Diabetic Sensory Polyneuropathy
Bibliographic record
Abstract
OBJECTIVE: Cooling detection threshold testing may be an important quantitative method for assessing polyneuropathy, in that it has traditionally been viewed as a measure of small-fiber involvement. The present study sought to determine the agreement between two common testing devices and to determine whether these are concordant in their association with predictor variables for diabetic sensory polyneuropathy. RESEARCH DESIGN AND METHODS: A total of 83 patients with diabetes (10 patients with type 1 diabetes and 73 patients with type 2 diabetes) and a wide spectrum of diabetic sensory polyneuropathy severity underwent concurrent cooling detection threshold testing using the Medoc and CASE IV instruments. Common predictor variables for diabetic sensory polyneuropathy were measured on the same day. RESULTS: Measurements of cooling detection thresholds by both instruments were highly correlated (Spearman's correlation coefficient 0.81, P < 0.001) and demonstrated a high degree of agreement by the method of Bland and Altman (95% distribution critical values for the difference in cooling detection thresholds, +7.5 and -5.6 degrees C). Cooling detection thresholds by both instruments were strongly correlated with clinical indicators of large-fiber neuropathy but not with the symptoms of small-fiber neuropathy (pain). CONCLUSIONS: These two instruments available for assessment of cooling detection thresholds are interchangeable for research in diabetic sensory polyneuropathy. However, this modality is equivalent to other modalities of quantitative sensory threshold testing in its association with indicators of large-fiber neuropathy and does not seem to provide an advantage for the prediction of small-fiber involvement.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".