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Record W4252449423 · doi:10.1155/2009/106875

Canadian Pain Society Conference

2009· article· en· W4252449423 on OpenAlexafffundabout
André Speaker, Serge Marchand, Michael McGillion, Manon Choinière, James L. Henry, Juliana Barcellos de Souza, André Bélanger, P. Baer, Mdcm Frcpc Facr, Mdcm Facr, Pierre Dolbec, Avinash Sinha, Mbchb Frca, Joel Katz, Christian Cloutier, Aline Boulanger, Mary‐Ann Fitzcharles, M Chb

Bibliographic record

VenuePain Research and Management · 2009
Typearticle
Languageen
FieldMedicine
TopicSpine and Intervertebral Disc Pathology
Canadian institutionsMontreal General HospitalOntario Rheumatology AssociationOntario Medical AssociationMcGill UniversityUniversité de MontréalCanadian Rheumatology AssociationUniversité de SherbrookeMcMaster UniversityYork University
FundersPfizer CanadaWayne State UniversityPfizerAmerican Osteopathic Association
KeywordsMedicinePsychologyPhysical therapy

Abstract

fetched live from OpenAlex

Learning Objectives: 1. Understanding the basic neurophysiological mechanisms of fibromyalgia. 2. Recognizing the factors predisposing to chronic widespread pain. 3. Being introduce to the factors implicated in the heterogeneity of fibromyalgic patients and their responses to treatment. BRIEF DESCRIPTION: Patients suffering from fibromyalgia present diffuse pain symptoms that are not characteristics of neurogenic pain. However, some neurophysiological mechanisms, such as a disturbance of endogenous pain modulation systems, may help understanding the mechanisms implicated. During this talk I will introduce some of our work on different factors that are affecting the efficacy of these endogenous pain modulation mechanisms and discuss how diffuse pain may result from a central imbalance in endogenous excitatory and inhibitory mechanisms. Recent data will be presented supporting our understanding of the neurophysiological mechanisms of diffuse pain in fibromyalgia with some references to the clinical signs observed in the fibromyalgia patients.

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)0.003
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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.869
Threshold uncertainty score0.918

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
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 head0.084
GPT teacher head0.379
Teacher spread0.295 · 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 designNot applicable
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

Citations1
Published2009
Admission routes3
Has abstractyes

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