870-P: Long-Term Efficacy and Safety of Crisugabalin Besilate in Chinese patients with Diabetic Peripheral Neuropathic Pain
Bibliographic record
Abstract
Introduction & Objective: Chronic diabetic peripheral neuropathic pain (DPNP) exerts serious adverse effects on patients' emotions and quality of life. Crisugabalin has shown favorable efficacy and safety in a phase II/III study in Chinese patients with DPNP. In this open-label extension study, we further evaluated the long-term safety and efficacy of Crisugabalin Besilate. Methods: A total of 301 patients with DPNP from the previous phase II/III trial were enrolled in this 52-week open-label study. Patients received Crisugabalin Besilate capsule 40mg twice daily. Pain intensity was measured with Short-Form McGill Pain Questionnaire (SF-MPQ), including Pain Rating Index (PRI), Visual Analogue Scale (VAS), and Present Pain Intensity (PPI). Results: All the efficacy indicators of SF-MPQ showed continuous decrease throughout the trial, and the scores at week 52 were significantly different compared with baseline. The mean changes in PRI and VAS from baseline at week 52 were -2.5±3.97 and -23.4±19.38, respectively, and that the proportion of subjects with PPI ≤ 1 increased by 19% from baseline. 86.4% subjects experienced a total of 1095 Treatment Emergent Adverse Events (TEAEs) during the study, including 203 drug-related TEAEs with the incidence of 38.9%. The most common drug-related TEAEs were dizziness (27.2%) and somnolence (8.3%), and the majority were mild to moderate. The incidence of TEAEs leading to dose reduction was 14.3%. Conclusions: Long-term treatment with Crisugabalin Besilate is effective and safe for pain relief in Chinese patients with DPNP. Disclosure T. Zhang: None. X. Guo: None. H. Li: None. J. Ma: None. L. Yukun: None. C. Jiang: None. J. Liu: None. Funding Haisco Pharmaceutical Group, Sichuan, China
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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 | 0.001 |
| 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 | 0.000 |
| 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".