Ranirestat for the Management of Diabetic Sensorimotor Polyneuropathy
Bibliographic record
Abstract
OBJECTIVE Aldose reductase inhibitors (ARIs) are potential disease modifiers for diabetes complications. We aimed to determine whether ranirestat, an ARI, could slow or reverse the course of diabetic sensorimotor polyneuropathy (DSP). RESEARCH DESIGN AND METHODS A total of 549 patients with DSP were randomly assigned to treatment with placebo or 10, 20, or 40 mg/day ranirestat for 52 weeks in this multicenter, double-blind study. Efficacy was evaluated by nerve conduction studies, the modified Toronto Clinical Neuropathy Score (mTCNS), and quantitative sensory tests (QSTs). RESULTS At week 52, the summed sensory (bilateral sural plus proximal median sensory) nerve conduction velocity (NCV) did not show significant changes from baseline (2.0 m/s for placebo compared with 3.2–3.8 m/s for ranirestat). Significant improvement in the summed motor (peroneal, tibial, and median) NCV was observed with 20 and 40 mg/day ranirestat treatment at week 12 (P ≤ 0.05) and at weeks 24 and 36 and in peroneal motor NCV at weeks 36 and 52 (P ≤ 0.05) for the 20 mg/day ranirestat group. The mTCNS and QST results did not differ among the groups during the study. Ranirestat was well tolerated with no pertinent differences in drug-related adverse events or in effects on clinical laboratory parameters, vital signs, or electrocardiograms among the four groups. CONCLUSIONS Treatment with ranirestat appears to have an effect on motor nerve function in mild to moderate DSP, but the results of this study failed to show a statistically significant difference in sensory nerve function relative to placebo.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".