Long-Term Outcomes in the Management of Painful Diabetic Neuropathy
Bibliographic record
Abstract
BACKGROUND: Painful diabetic neuropathy (PDN) is a frequent complication of diabetes mellitus. Current treatment recommendations are based on short-term trials, generally of ≤3 months' duration. Limited data are available on the long-term outcomes of this chronic disease. The objective of this study was to determine the long-term clinical effectiveness of the management of chronic PDN at tertiary pain centres. METHODS: From a prospective observational cohort study of patients with chronic neuropathic non-cancer pain recruited from seven Canadian tertiary pain centres, 60 patients diagnosed with PDN were identified for analysis. Data were collected according to Initiative on Methods, Measurement, and Pain Assessment in Clinical Trials guidelines including the Brief Pain Inventory. RESULTS: At 12-month follow-up, 37.2% (95% confidence interval [CI], 23.0-53.3) of 43 patients with complete data achieved pain reduction of ≥30%, 51.2% (95% CI, 35.5-66.7) achieved functional improvement with a reduction of ≥1 on the Pain Interference Scale (0-10, Brief Pain Inventory) and 30.2% (95% CI, 17.2-46.1) had achieved both these measures. Symptom management included at least two medication classes in 55.3% and three medication classes in 25.5% (opioids, antidepressants, anticonvulsants). CONCLUSIONS: Almost one-third of patients being managed for PDN in a tertiary care setting achieve meaningful improvements in pain and function in the long term. Polypharmacy including analgesic antidepressants and anticonvulsants were the mainstays of effective symptom management.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".