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Record W1459322896 · doi:10.1017/cbo9781139152211.014

Central pain symptoms in multiple sclerosis

2013· book-chapter· en· W1459322896 on OpenAlexaff
Scott E. Jarvis, Bradley J. Kerr

Bibliographic record

VenueCambridge University Press eBooks · 2013
Typebook-chapter
Languageen
FieldMedicine
TopicPain Mechanisms and Treatments
Canadian institutionsWestern UniversityUniversity of Calgary
Fundersnot available
KeywordsNociceptorNeuroscienceTransient receptor potential channelNeuropathic painSensitizationTransduction (biophysics)Metabotropic receptorG protein-coupled receptorNociceptionInhibitory postsynaptic potentialSensory systemReceptorChronic painSignal transductionMedicineBiologyCell biologyInternal medicineBiophysics

Abstract

fetched live from OpenAlex

This chapter explores the cellular and genetic mechanisms important in the development of neuropathic pain and other forms of chronic pain related to the phenomenon of sensitization. Peripheral sensitization contributes to pain hypersensitivity found at the location of tissue damage and/or inflammation. The chapter reviews the events that underlie pain as well as the anatomical and pharmacological basis for nociceptive sensations and chronic pain. Nociceptor excitation via various transient receptor potential (TRP) channels can result from a number of contributing processes. Transduction of mechanical, thermal, and chemical stimuli begins with membrane depolarization, which, if sufficient, transforms into an action potential. There are three classes of cell surface proteins at the sensory neuron important for sensory transduction: ion channels, metabotropic G protein-coupled receptors (GPCRs), and receptors for neurotrophins or cytokines. An important concept in central modulation of pain is the central inhibitory pathways and networks.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.020
Threshold uncertainty score0.068

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0200.005

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 head0.034
GPT teacher head0.190
Teacher spread0.156 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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

Citations1
Published2013
Admission routes1
Has abstractyes

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