MétaCan
Menu
Back to cohort
Record W4411995564 · doi:10.1136/bcr-2025-265205

Multiday intravenous ketamine infusion therapy for the management of central sensitisation syndrome secondary to chronic chemotherapy–induced peripheral neuropathic pain

2025· article· en· W4411995564 on OpenAlexaff
Mohammad F. Abdullah, May Ong

Bibliographic record

VenueBMJ Case Reports · 2025
Typearticle
Languageen
FieldMedicine
TopicCancer Treatment and Pharmacology
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineKetamineChronic painAnesthesiaRefractory (planetary science)AnalgesicNeuropathic painCancer painCancerInternal medicinePhysical therapy

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.810
Threshold uncertainty score0.506

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.018
GPT teacher head0.318
Teacher spread0.300 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueBMJ Case ReportsSame topicCancer Treatment and PharmacologyFrench-language works237,207