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Record W4391058195 · doi:10.1016/j.ynpai.2024.100150

Pain-sensorimotor interactions: New perspectives and a new model

2024· article· en· W4391058195 on OpenAlexaff
Greg M. Murray, Barry J. Sessle

Bibliographic record

VenueNeurobiology of Pain · 2024
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPsychologyConceptualizationCognitive psychologyBiopsychosocial modelChronic painCognitionNeuroscienceCognitive sciencePsychotherapistComputer science

Abstract

fetched live from OpenAlex

How pain and sensorimotor behavior interact has been the subject of research and debate for many decades. This article reviews theories bearing on pain-sensorimotor interactions and considers their strengths and limitations in the light of findings from experimental and clinical studies of pain-sensorimotor interactions in the spinal and craniofacial sensorimotor systems. A strength of recent theories is that they have incorporated concepts and features missing from earlier theories to account for the role of the sensory-discriminative, motivational-affective, and cognitive-evaluative dimensions in pain-sensorimotor interactions. Findings acquired since the formulation of these recent theories indicate that additional features need to be considered to provide a more comprehensive conceptualization of pain-sensorimotor interactions. These features include biopsychosocial influences that range from biological factors such as genetics and epigenetics to psychological factors and social factors encompassing environmental and cultural influences. Also needing consideration is a mechanistic framework that includes other biological factors reflecting nociceptive processes and glioplastic and neuroplastic changes in sensorimotor and related brain and spinal cord circuits in acute or chronic pain conditions. The literature reviewed and the limitations of previous theories bearing on pain-sensorimotor interactions have led us to provide new perspectives on these interactions, and this has prompted our development of a new concept, the Theory of Pain-Sensorimotor Interactions (TOPSMI) that we suggest gives a more comprehensive framework to consider the interactions and their complexity. This theory states that pain is associated with plastic changes in the central nervous system (CNS) that lead to an activation pattern of motor units that contributes to the individual’s adaptive sensorimotor behavior. This activation pattern takes account of the biological, psychological, and social influences on the musculoskeletal tissues involved in sensorimotor behavior and on the plastic changes and the experience of pain in that individual. The pattern is normally optimized in terms of biomechanical advantage and metabolic cost related to the features of the individual’s musculoskeletal tissues and aims to minimize pain and any associated sensorimotor changes, and thereby maintain homeostasis. However, adverse biopsychosocial factors and their interactions may result in plastic CNS changes leading to less optimal, even maladaptive, sensorimotor changes producing motor unit activation patterns associated with the development of further pain. This more comprehensive theory points towards customized treatment strategies, in line with the management approaches to pain proposed in the biopsychosocial model of 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 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)0.003
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0030.002
Science and technology studies0.0020.021
Scholarly communication0.0070.020
Open science0.0040.005
Research integrity0.0050.009
Insufficient payload (model declined to judge)0.0070.002

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 head0.022
GPT teacher head0.304
Teacher spread0.282 · 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 designTheoretical or conceptual
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

Citations25
Published2024
Admission routes1
Has abstractyes

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