Quantitative sensory testing in type 1 diabetic patients with painful and painless diabetic neuropathy
Bibliographic record
Abstract
The mechanism underlying the development of painful diabetic neuropathy (DN) is unknown. The aim of this study was to compare quantitative sensory testing (QST) characteristics of patients with painful and painless DN and to correlate QST measures with DN pain. Fifty type 1 diabetic patients with DN (30 with painful DN and 20 with painless DN) and 32 age-matched non-diabetic controls were included in this study. For all patients and controls, a detailed assessment of DN was performed which comprised McGill visual analogue scale (McGill VAS) for pain, neuropathy symptom profile and neuropathy disability score (NDS), quantitative sensory testing in form of cold, warm and vibration perception thresholds, nerve conduction studies (NCS), deep-breathing hear rate variability and Neuropad staining scores. Measures of QST, NCS and DB-HRV were correlated with McGill VAS for pain. There were no significant differences in cold, warm and vibration perception thresholds, NCS, DB-HRV and Neuropad scores between patients with painful and painless DN. Cold (r=-0.57, P=0.005), warm (r=0.47, P=0.026) and DB-HRV (r=0.50, P=0.023), however, correlated significantly with McGill VAS scores of pain. In conclusion, Quantitative sensory testing is a helpful tool to identify small nerve fibres damage of DN, correlates with pain intensity but cannot differentiate between painful and painless DN. Both central and peripheral neural injury could be implicated in the genesis of DN pain. [Dis Mol Med 2016; 4(3.000): 24-30]
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".