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Record W4401108535 · doi:10.1080/15360288.2024.2383423

Methadone for Pain Management in Chemotherapy-Induced Peripheral Neuropathy: A Retrospective Review

2024· review· en· W4401108535 on OpenAlexaff
Christiane Boen, Julia Ridley, Philippa Hawley

Bibliographic record

VenueJournal of Pain & Palliative Care Pharmacotherapy · 2024
Typereview
Languageen
FieldMedicine
TopicCancer Treatment and Pharmacology
Canadian institutionsSpinal Cord Injury BCUniversity of British Columbia
Fundersnot available
KeywordsMedicineDuloxetineMethadoneChemotherapy-induced peripheral neuropathyNeuropathic painPeripheral neuropathyCancer painAnalgesicChemotherapyAnesthesiaIntensive care medicineInternal medicineCancerAlternative medicineDiabetes mellitus

Abstract

fetched live from OpenAlex

Chemotherapy-Induced Peripheral Neuropathy (CIPN) refers to damage of peripheral nerve fibers due to the use of neurotoxic chemotherapy to treat various cancers. It occurs in more than 30% of patients and only duloxetine has currently been identified to show limited efficacy in symptomatic treatment of CIPN. Opioids have traditionally been used to treat cancer pain, and there is evidence for their use in treatment of peripheral neuropathic pain from other causes. With a similar mechanism of action to duloxetine, methadone has rationale for treating neuropathic pain. This study is a retrospective chart review to evaluate the outcomes of using methadone for CIPN pain. Out of 31 patients, 65% felt that methadone was an effective treatment, 19% felt that it was ineffective, and 16% felt that it was partially or temporarily effective. These results suggest that analgesic response to methadone varies between patients, but that it has a potential role in painful CIPN. Its advantages for long-term use include low cost and lack of metabolites. Potential risks include a long half-life, drug interactions, and potential for QT prolongation at high doses. Prospective studies should be conducted to evaluate the role of methadone in CIPN pain management more comprehensively.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.875
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.089
GPT teacher head0.466
Teacher spread0.377 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations4
Published2024
Admission routes1
Has abstractyes

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