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Record W4403535974 · doi:10.1089/pmr.2024.0049

Safe and Effective Treatment of Refractory Cancer Pain with Chronic Oral Ketamine: A Case Series

2024· article· en· W4403535974 on OpenAlexaboutno aff
Dmitry Kozhevnikov, Lava Kareem, Trinh Bui

Bibliographic record

VenuePalliative Medicine Reports · 2024
Typearticle
Languageen
FieldMedicine
TopicPain Mechanisms and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsRefractory (planetary science)MedicineCancerKetamineSeries (stratigraphy)Chronic painAnesthesiaPhysical therapyInternal medicineGeology

Abstract

fetched live from OpenAlex

Introduction: A growing body of evidence supports the off-label use of subanesthetic or low-dose oral ketamine for chronic pain. However, the safety and efficacy of long-term treatment remain unclear. This case series describes one palliative care (PC) clinic’s experience using long-term oral ketamine to treat refractory cancer pain. Methods: All patients ( n = 3) treated with oral ketamine in one PC clinic between 2022 and 2024 for adult cancer pain were reviewed. Pain scores were collected using the Edmonton Symptom Assessment Scale numeric rating scale as part of routine follow-up visits. Results: All patients reported improvement in cancer-related pain with the addition of oral ketamine. The total daily dose ranged from 30 mg to 320 mg. One patient experienced mild dissociative effects and dizziness, which resolved after decreasing the total daily dose. Treatment was maintained beyond 3 months in 2/3 cases, and the longest course of therapy was 18 months. The most significant logistical challenge was related to supply chain issues limiting availability at community pharmacies. Conclusion: Chronic treatment with oral ketamine as a coanalgesic for refractory cancer pain was well tolerated. All patients reported improved pain scores with the addition of oral ketamine. Ongoing research is needed to further describe the risks and benefits of long-term oral ketamine treatment.

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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.360
Threshold uncertainty score0.661

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.020
GPT teacher head0.331
Teacher spread0.311 · 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 designObservational
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

Citations0
Published2024
Admission routes1
Has abstractyes

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