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Record W2314425055 · doi:10.1155/2000/437256

Common Mechanisms Underlying Opioid Tolerance and Dependence and Neuropathic Pain: Role of Metabotropic Glutamate Receptors

2000· article· en· W2314425055 on OpenAlexaff
Marian E. Fundytus

Bibliographic record

VenuePain Research and Management · 2000
Typearticle
Languageen
FieldMedicine
TopicPain Mechanisms and Treatments
Canadian institutionsMcGill University
Fundersnot available
KeywordsNeuropathic painMetabotropic glutamate receptorMetabotropic glutamate receptor 2Glutamate receptorOpioidNeuroscienceMetabotropic glutamate receptor 5Metabotropic glutamate receptor 1Kainate receptorMetabotropic receptorMetabotropic glutamate receptor 7PharmacologyMedicinePsychologyReceptorInternal medicineAMPA receptor

Abstract

fetched live from OpenAlex

It has been suggested that opioid tolerance and dependence share common mechanisms with neuropathic pain. This short review deals with the role of glutamate and glutamate receptors in opioid tolerance and dependence, and neuropathic pain. Particular attention is given to the role of metabotropic glutamate receptors (mGluRs). First, the different types of glutamate receptors, which include N ‐methyl‐D‐aspartate, alpha‐amino‐3‐hydroxyl‐5‐methyl‐ isoxazole‐4‐propionic acid, kainate and mGluRs, are described. Following this, evidence suggesting that these receptors are involved in opioid tolerance and dependence are summarized. At the end of this section, a model that has been previously proposed to explain mechanisms by which mGluRs may be involved in opioid tolerance and dependence are described. Next is a discussion of the evidence suggesting that glutamate receptors are similarly involved in neuropathic pain, and also in opioid sensitivity associated with neuropathic pain. Again, a hypothetical model used to explain mechanisms by which mGluRs may be involved in neuropathic pain is briefly described. The relevance of the data is discussed in terms of some of the clinical implications of the material presented in the article.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.966
Threshold uncertainty score0.504

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.042
GPT teacher head0.320
Teacher spread0.278 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreEmpirical

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

Citations0
Published2000
Admission routes1
Has abstractyes

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