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Neuropathic Pain: Mechanisms, Sex Differences, and Potential Therapies for a Global Problem

2023· review· en· W4317567454 on OpenAlexafffund
Shahrzad Ghazisaeidi, Milind M. Muley, Michael W. Salter

Bibliographic record

VenueThe Annual Review of Pharmacology and Toxicology · 2023
Typereview
Languageen
FieldMedicine
TopicPain Mechanisms and Treatments
Canadian institutionsHospital for Sick ChildrenUniversity of Toronto
FundersCanadian Institutes of Health ResearchHospital for Sick ChildrenKrembil FoundationUniversity of Toronto
KeywordsChronic painNeuropathic painAnalgesicMedicineNeuroscienceBioinformaticsPsychologyBiologyPharmacology

Abstract

fetched live from OpenAlex

The study of chronic pain continues to generate ever-increasing numbers of publications, but safe and efficacious treatments for chronic pain remain elusive. Recognition of sex-specific mechanisms underlying chronic pain has resulted in a surge of studies that include both sexes. A predominant focus has been on identifying sex differences, yet many newly identified cellular mechanisms and alterations in gene expression are conserved between the sexes. Here we review sex differences and similarities in cellular and molecular signals that drive the generation and resolution of neuropathic pain. The mix of differences and similarities reflects degeneracy in peripheral and central signaling processes by which neurons, immune cells, and glia codependently drive pain hypersensitivity. Recent findings identifying critical signaling nodes foreshadow the development of rationally designed, broadly applicable analgesic strategies. However, the paucity of effective, safe pain treatments compels targeted therapies as well to increase therapeutic options that help reduce the global burden of suffering.

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 score0.928
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
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 head0.044
GPT teacher head0.378
Teacher spread0.334 · 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

Citations160
Published2023
Admission routes2
Has abstractyes

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