Tetrodotoxin for Chemotherapy-Induced Neuropathic Pain: A Randomized, Double-Blind, Placebo-Controlled, Parallel-Dose Finding Trial
Bibliographic record
Abstract
Tetrodotoxin (TTX) has emerged as a potentially efficacious agent for chemotherapy-induced neuropathic pain (CINP), a prevalent, debilitating condition often resistant to analgesics. This randomized, double-blind, dose-finding study was undertaken to explore safety and trends in efficacy of four TTX doses and to identify a dose for further study. One hundred and twenty-five patients with taxane- or platinum-related CINP received subcutaneous placebo or TTX (7.5 µg twice daily (BID), 15 µg BID, 30 µg once daily (QD), 30 µg BID) for four consecutive days. Primary outcome measure was average patient-reported Numeric Pain Rating Scale (NPRS) score during Days 21–28 post-treatment. Changes in mean NPRS score were not statistically different between cohorts, due to small trial size and influence of a few robust placebo responders. Cumulative responder analysis showed significant difference from placebo with 30 µg BID cohort using the maximum response at any timepoint (p = 0.072), 5-day (p = 0.059), 10-day (p = 0.027), and 20-day (p = 0.071) rolling averages. In secondary quality of life (QOL) outcomes, 30 µg BID cohort also differed significantly from placebo in a number of SF-36 and CIPN20 subscales. Most adverse events (AE) were mild or moderate with oral paresthesia (29.6%) and oral hypoesthesia (24.8%) as most common.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".