Multiday intravenous ketamine infusion therapy for the management of central sensitisation syndrome secondary to chronic chemotherapy–induced peripheral neuropathic pain
Bibliographic record
Abstract
Ketamine infusion therapy is increasingly being used as an effective treatment for chronic pain syndromes, including central sensitisation syndrome (CSS) or nociplastic pain. On the contrary, chemotherapy-induced peripheral neuropathy (CIPN) is a common but poorly understood condition arising secondary to cancer treatment complications, which poses significant challenges in its management due to limited therapeutic options. We present a case of a man in his 60s with chronic CIPN, later complicated by CSS and post-COVID-19 symptoms treated with a multiday subanaesthetic ketamine infusion, resulting in a clinically significant and sustained long-term improvement in function and pain control, for pain due to CSS and CIPN.This case highlights the use of multiday ketamine infusion therapy for the management of CSS and post-COVID-19 symptoms in a patient with well-documented severe treatment refractory CIPN. It demonstrates the growing evidence for ketamine as an analgesic agent for chronic pain, with potential considerations to expand its use for other indications. His response to ketamine infusion may implicate the possibility of a unifying mechanism in patients with nociplastic pain or CSS, post- COVID-19 symptoms and chronic CIPN.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".