Late Breaking Abstract - Efficacy and safety of nalbuphine extended-release in refractory chronic cough: results from the phase 2a RIVER trial
Bibliographic record
Abstract
Background: Nalbuphine extended-release (NAL ER) acts on the cough reflex arc centrally and peripherally as a kappa agonist and mu antagonist (KAMA), targeting opioid receptors involved in controlling chronic cough. Aims and Objectives: The RIVER trial ( NCT05962151 ) evaluated NAL ER in patients with refractory chronic cough (RCC). Methods: This double-blind, placebo (PBO)-controlled, crossover study enrolled patients with RCC stratified by 24-h cough frequency at screening (10-19 coughs/h [moderate] and ≥20 coughs/h [severe]) measured by a cough monitor. Patients were randomly assigned to 1 of 2 21-day treatment sequences: NAL ER to PBO or PBO to NAL ER, with a 21-d washout between treatments. NAL ER was titrated every 7 d (27 mg, 54 mg, and 108 mg, all BID). The primary endpoint was relative change from baseline in 24-h objective cough frequency at day 21 in all patients. Results: Sixty-six patients were randomized (mean age, 60.2 y; mean cough frequency, 34.7 coughs/h). NAL ER 108 mg and PBO reduced 24-h cough frequency 65% and 9% at day 21, respectively (PBO-adjusted improvement of 57%; P<.0001). Moderate and severe cough frequency subgroups achieved PBO-adjusted improvements of 76% and 50%, respectively (both P<.0001). At day 7, mean (SD) 24-hour cough frequency significantly decreased with 27 mg NAL ER BID (–15.67 [13.61]; P<.0001). No serious treatment-emergent adverse events (TEAEs) were reported; 10 patients discontinued treatment due to TEAEs. The most common (≥5%) TEAEs were constipation, nausea, somnolence, headache, dizziness, and fatigue. Conclusions: The significant reduction in 24-h cough frequency supports further investigation of NAL ER for RCC.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".