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Record W3130785468 · doi:10.1080/14740338.2021.1889513

A safety review of approved intrathecal analgesics for chronic pain management

2021· review· en· W3130785468 on OpenAlexaff
Alan Chalil, Michael D. Staudt, Tessa Harland, Elizabeth M. Leimer, Ravneet Bhullar, Charles E. Argoff

Bibliographic record

VenueExpert Opinion on Drug Safety · 2021
Typereview
Languageen
FieldMedicine
TopicPain Management and Opioid Use
Canadian institutionsLondon Health Sciences CentreWestern University
Fundersnot available
KeywordsMedicineChronic painAdverse effectDiscontinuationOpioidMorphineAnalgesicAnesthesiaIntensive care medicinePharmacologyInternal medicinePhysical therapy

Abstract

fetched live from OpenAlex

Introduction: Intrathecal (IT) drug therapy is an effective treatment option for patients with chronic pain of malignant or nonmalignant origin, with an established safety profile and fewer adverse effects compared to oral or parenteral pain medications. Morphine (a μ-opioid receptor agonist) and ziconotide (a non-opioid calcium channel antagonist) are the only IT agents approved by the U.S. Food and Drug Administration for the treatment of chronic pain. Although both are considered first-line IT therapies, each drug has unique properties and considerations.Areas Covered: This review will evaluate the pivotal trials that established the use of morphine and ziconotide as first-line IT therapy for patients with chronic pain, as well as safety and efficacy data generated from various retrospective and prospective studies.Expert Opinion: Morphine and ziconotide are effective IT therapies for patients with chronic malignant or nonmalignant pain that is refractory to other interventions. IT ziconotide is recommended as a first-line therapy due to its efficacy and avoidance of many adverse effects commonly associated with opioids. The use of IT morphine is also considered first-line; however, the risks of respiratory depression, withdrawal with drug discontinuation or pump malfunction, and the development of tolerance require careful patient selection and management.

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)0.003
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 score0.671
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0000.001
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 head0.041
GPT teacher head0.367
Teacher spread0.326 · 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

Citations15
Published2021
Admission routes1
Has abstractyes

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