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Record W2000599011 · doi:10.1124/mi.9.5.7

In Search of Analgesia: Emerging Poles of GPCRs in Pain

2009· review· en· W2000599011 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueMolecular Interventions · 2009
Typereview
Languageen
FieldNeuroscience
TopicNeuropeptides and Animal Physiology
Canadian institutionsMcGill University
FundersNational Institute of Neurological Disorders and Stroke
KeywordsG protein-coupled receptorReceptorPopulationAnalgesic agentsMedicineAnalgesicGenomeComputational biologyBioinformaticsChronic painBiologyNeurosciencePharmacologyGeneGeneticsInternal medicine

Abstract

fetched live from OpenAlex

Of all clinically marketed drugs, greater than thirty percent are modulators of G protein-coupled receptors (GPCRs). Nearly 400 GPCRs (i.e., excluding odorant and light receptors) are encoded within the human genome, but only a small fraction of these seven-transmembrane proteins have been identified as drug targets. Chronic pain affects more than one-third of the population, representing a substantial societal burden in use of health care resources and lost productivity. Furthermore, currently available treatments are often inadequate, underscoring the significant need for better therapeutic strategies. The expansion of the identified human GPCR repertoire, coupled with recent insights into the function and structure of GPCRs, offers new opportunities for the development of novel analgesic therapeutics.

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.

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)0.000
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: Review · Consensus signal: Review
Teacher disagreement score0.875
Threshold uncertainty score0.728

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
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 head0.113
GPT teacher head0.405
Teacher spread0.292 · 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