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Record W2230654545 · doi:10.1155/2005/860354

Neural Mechanisms of Temporomandibular Joint and Masticatory Muscle Pain: A Possible Role for Peripheral Glutamate Receptor Mechanisms

2005· review· en· W2230654545 on OpenAlexaff
David K. Lam, Barry J. Sessle, Brian E. Cairns, James W. Hu

Bibliographic record

VenuePain Research and Management · 2005
Typereview
Languageen
FieldMedicine
TopicPain Mechanisms and Treatments
Canadian institutionsUniversity of Toronto
FundersNational Institute of Dental and Craniofacial Research
KeywordsTemporomandibular jointIonotropic effectGlutamate receptorFibromyalgiaMedicineNociceptionMetabotropic glutamate receptor 5CraniofacialPeripheralChronic painNeuroscienceReceptorInternal medicineMetabotropic glutamate receptorPsychologyPathologyPhysical therapy

Abstract

fetched live from OpenAlex

The purpose of the present review is to correlate recent knowledge of the role of peripheral ionotropic glutamate receptors in the temporomandibular joint and muscle pain from animal and human experimental pain models with findings in patients. Chronic pain is common, and many people suffer from chronic pain conditions involving deep craniofacial tissues such as temporomandibular disorders or fibromyalgia. Animal and human studies have indicated that the activation of peripheral ionotropic glutamate receptors in deep craniofacial tissues may contribute to muscle and temporomandibular joint pain and that sex differences in the activation of glutamate receptors may be involved in the female predominance in temporomandibular disorders and fibromyalgia. A peripheral mechanism involving autocrine and/or paracrine regulation of nociceptive neuronal excitability via injury or inflammation-induced release of glutamate into peripheral tissues that may contribute to the development of craniofacial pain is proposed.

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: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.079
GPT teacher head0.363
Teacher spread0.284 · 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
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

Citations89
Published2005
Admission routes1
Has abstractyes

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