MétaCan
Menu
Back to cohort
Record W2019042786 · doi:10.1097/ajp.0b013e318164bb15

Toward a Biopsychomotor Conceptualization of Pain

2008· review· en· W2019042786 on OpenAlexaff
Michael Sullivan

Bibliographic record

VenueClinical Journal of Pain · 2008
Typereview
Languageen
FieldMedicine
TopicPediatric Pain Management Techniques
Canadian institutionsMcGill University
Fundersnot available
KeywordsConceptualizationPhenomenonSensationPsychologyPerceptionExperiential learningChronic painCognitive psychologyPain catastrophizingNeuroscienceEpistemologyArtificial intelligenceComputer science

Abstract

fetched live from OpenAlex

OBJECTIVES: Nearly 400 years ago, René Descartes proposed a model of pain perception that characterized pain as a purely physical phenomenon, devoid of psychologic influence. The characterization of pain as an exclusively sensory (or experiential) phenomenon continues to dominate current conceptualizations of pain. METHODS: This paper advances the view that the exclusive focus on pain sensation or experience as the essential feature of the pain system has given rise to conceptual frameworks that are incomplete and flawed. It is argued that individuals with pain differ from individuals without pain not only in how they "feel" but they differ in how they "behave." RESULTS: Arguments are put forward advocating for a biopsychomotor conceptualization of pain where pain behaviors are construed as integral components of the pain system. The biopsychomotor model proposes that at least 3 partially independent behavioral subsystems are integral components of pain. These include communicative pain behaviors, protective pain behaviors, and social response behaviors. Evidence is reviewed suggesting that different dimensions of pain behavior are functionally distinct, and questions are raised about the nature of motor programs responsible for the elicitation and maintenance of different forms of pain behavior. DISCUSSION: Clinical and theoretical implications of a biopsychomotor conceptualization of pain are discussed.

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)0.016
metaresearch head score (Gemma)0.016
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.865
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0160.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0010.001
Science and technology studies0.0000.000
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 head0.196
GPT teacher head0.465
Teacher spread0.269 · 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 designNot applicable
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

Citations127
Published2008
Admission routes1
Has abstractyes

Explore more

Same venueClinical Journal of PainSame topicPediatric Pain Management TechniquesFrench-language works237,207