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Record W4240632054 · doi:10.21037/apm-20-972

Palliative ketamine: the use of ketamine in central post-stroke pain syndrome—a case report

2020· article· en· W4240632054 on OpenAlexaboutno aff
Rachel Angstadt, Shawn Esperti, Andrew Mangano, Stephen M. Meyer

Bibliographic record

VenueAnnals of Palliative Medicine · 2020
Typearticle
Languageen
FieldMedicine
TopicTreatment of Major Depression
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineKetamineNeuropathic painAnesthesiaOpioidCancer painStroke (engine)Palliative careInternal medicineCancer

Abstract

fetched live from OpenAlex

Ketamine has played a versatile role in medicine due to its wide spectrum of uses in history including use in sedation, catalepsy, somatic analgesia, bronchodilation, and recent trial in complex chronic pain syndromes. There is very little, if any, discussion in the literature of ketamine use in stroke, particularly in improving symptoms of pain after stroke. We present a case of a 40-year-old female with a past medical history of right-sided thalamic ischemic stroke complicated by Central post-stroke pain syndrome (CPSP) presented for refractory severe diffuse pain causing debility and immobility. The patient failed outpatient medical therapy consisting of anxiolytics, serotonin, and norepinephrine reuptake inhibitors. This led to increased opioid use which resulted in dependence and opioid-hyperalgesia. Upon admission, the patient was unable to sit still, with severe, sharp 10/10 pain localized to her left lower extremity. Palliative medicine was consulted for management of refractory central neuropathic pain. Inpatient oral ketamine was initiated, and titrated over the patient's hospital course. During this time, the patient showed marked improvement in GAD-7, PHQ-9, and Short Mcgill pain scores while significantly decreasing opioid requirements. We present this case to demonstrate how oral ketamine usage in centrally mediated neuropathic pain such as in CPSP can lead to pain control, decreased opioid usage, and overall improved quality of life.

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)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.137
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.168
GPT teacher head0.364
Teacher spread0.196 · 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.

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

Citations5
Published2020
Admission routes1
Has abstractyes

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