4. Painful diabetic polyneuropathy
Bibliographic record
Abstract
INTRODUCTION: Pain as a symptom of diabetic polyneuropathy (DPN) significantly lowers quality of life, increases mortality and is the main reason for patients with diabetes to seek medical attention. The number of people suffering from painful diabetic polyneuropathy (PDPN) has increased significantly over the past decades. METHODS: The literature on the diagnosis and treatment of diabetic polyneuropathy was retrieved and summarized. RESULTS: The etiology of PDPN is complex, with primary damage to peripheral nociceptors and altered spinal and supra-spinal modulation. To achieve better patient outcomes, the mode of diagnosis and treatment of PDPN evolves toward more precise pain-phenotyping and genotyping based on patient-specific characteristics, new diagnostic tools, and prior response to pharmacological treatments. According to the Toronto Diabetic Neuropathy Expert Group, a presumptive diagnosis of "probable PDPN" is sufficient to initiate treatment. Proper control of plasma glucose levels, and prevention of risk factors are essential in the treatment of PDPN. Mechanism-based pharmacological treatment should be initiated as early as possible. If symptomatic pharmacologic treatment fails, spinal cord stimulation (SCS) should be considered. In isolated cases, where symptomatic pharmacologic treatment and SCS are unsuccessful or cannot be used, sympathetic lumbar chain neurolysis and/or radiofrequency ablation (SLCN/SLCRF), dorsal root ganglion stimulation (DRGs) or posterior tibial nerve stimulation (PTNS) may be considered. However, it is recommended that these treatments be applied only in a study setting in a center of expertise. CONCLUSIONS: The diagnosis of PDPN evolves toward pheno-and genotyping and treatment should be mechanism-based.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.005 |
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".