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Record W2116972840 · doi:10.1177/10454411010120060101

Sensory and Affective Components of Orofacial Pain: Is it all in your Brain?

2001· review· en· W2116972840 on OpenAlexaff
Howard C. Tenenbaum, D. Mock, A. Gordon, M.B. Goklberg, Márcio Lima Grossi, David Locker, Karen D. Davis

Bibliographic record

VenueCritical Reviews in Oral Biology & Medicine · 2001
Typereview
Languageen
FieldMedicine
TopicPain Mechanisms and Treatments
Canadian institutionsToronto Western HospitalUniversity of TorontoMount Sinai Hospital
Fundersnot available
KeywordsOrofacial painChronic painNeuroscienceNeuropsychologySensory systemCognitionNeurophysiologyPsychologyMedicinePhysical medicine and rehabilitationPhysical therapy

Abstract

fetched live from OpenAlex

In this paper, we shall review several chronic orofacial pain conditions with emphasis on those that are essentially refractory to treatment. We shall present a review of current and past literature that describes the various pain phenomena as well as their underlying central mechanisms. New data concerning refractory pain will be used to underscore the importance of central processing of pain, with particular emphasis on neuropsychological and cognitive function and capacity that may play important roles in pain processing and maintenance of the pain state. Further, neurophysiological data showing that the anterior cingulate cortex (ACC) and other structures in the brain may play key roles in modulation of chronic pain will also be discussed. Although peripheral triggering events surely play an important role in initiating pain, the development of chronic and, in particular, refractory pain may depend on changes or malfunctions in the central nervous system. These changes may be quite subtle and require sophisticated approaches, such as functional MRI, to study them, as is now being done. New findings obtained therefore may lead to more rational and reliable treatment for orofacial pain.

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)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.977
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0060.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.278
GPT teacher head0.496
Teacher spread0.218 · 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.

Study designOther design
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

Citations33
Published2001
Admission routes1
Has abstractyes

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