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Record W1598400473 · doi:10.1186/1744-8069-1-1

Molecular Pain, a New Era of Pain Research and Medicine

2005· editorial· en· W1598400473 on OpenAlexaff
Jianguo G. Gu, Min Zhuo, Michael J. Caterina, Amy B. MacDermott, Annika B. Malmberg, Volker Neugebauer, Megumu Yoshimura

Bibliographic record

VenueMolecular Pain · 2005
Typeeditorial
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPharmacological Receptor Mechanisms and Effects
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPain medicineMedicineNeuroscienceChronic painMolecular biomarkersBioinformaticsPsychologyPsychiatryBiologyInternal medicine

Abstract

fetched live from OpenAlex

Molecular pain is a relatively new and rapidly expanding research field that represents an advanced step from conventional pain research. Molecular pain research addresses physiological and pathological pain at the cellular, subcellular and molecular levels. These studies integrate pain research with molecular biology, genomics, proteomics, modern electrophysiology and neurobiology. The field of molecular pain research has been rapidly expanding in the recent years, and has great promise for the identification of highly specific and effective targets for the treatment of intractable pain. Although several existing journals publish articles on classical pain research, none are specifically dedicated to molecular pain research. Therefore, a new journal focused on molecular pain research is needed. Molecular Pain, an Open Access, peer-reviewed, online journal, will provide a forum for molecular pain scientists to communicate their research findings in a targeted manner to others in this important and growing field.

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)0.004
metaresearch head score (Gemma)0.009
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: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.005
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.009
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.001
Science and technology studies0.0020.003
Scholarly communication0.0050.004
Open science0.0020.001
Research integrity0.0050.016
Insufficient payload (model declined to judge)0.0050.003

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 head0.017
GPT teacher head0.338
Teacher spread0.321 · 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
GenreEditorial

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

Citations67
Published2005
Admission routes1
Has abstractyes

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