Painful diabetic peripheral neuropathy: consensus recommendations on diagnosis, assessment and management
Bibliographic record
Abstract
Painful diabetic peripheral neuropathy (DPN) is common, is associated with significant reduction in quality of life and poses major treatment challenges to the practising physician. Although poor glucose control and cardiovascular risk factors have been proven to contribute to the aetiology of DPN, risk factors specific for painful DPN remain unknown. A number of instruments have been tested to assess the character, intensity and impact of painful DPN on quality of life, activities of daily living and mood. Management of the patient with DPN must be tailored to individual requirements, taking into consideration the co-morbidities and other factors. Pharmacological agents with proven efficacy for painful DPN include tricyclic anti-depressants, the selective serotonin and noradrenaline re-uptake inhibitors, anti-convulsants, opiates, membrane stabilizers, the anti-oxidant alpha-lipoic acid and topical agents including capsaicin. Current first-line therapies for painful DPN include tricyclic anti-depressants, the serotonin and noradrenaline re-uptake inhibitor duloxetine and the anti-convulsants pregabalin and gabapentin. When prescribing any of these agents, other co-morbidities and costs must be taken into account. Second-line approaches include the use of opiates such as synthetic opioid tramadol, morphine and oxycodone-controlled release. There is a limited literature with regard to combination treatment. In extreme cases of painful DPN unresponsive to pharmacotherapy, occasional use of electrical spinal cord stimulation might be indicated. There are a number of unmet needs in the therapeutic management of painful DPN. These include the need for randomized controlled trials with active comparators and data on the long-term efficacy of agents used, as most trials have lasted for less than 6 months. Finally, there is a need for appropriately designed studies to investigate non-pharmacological approaches.
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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 | 0.012 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.009 | 0.004 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.013 | 0.011 |
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".