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Record W2805216761 · doi:10.1111/dmcn.13930

Pathophysiology of chronic pain in cerebral palsy: implications for pharmacological treatment and research

2018· review· en· W2805216761 on OpenAlexaff
James A. Blackman, Camilla I. Svensson, Serge Marchand

Bibliographic record

VenueDevelopmental Medicine & Child Neurology · 2018
Typereview
Languageen
FieldMedicine
TopicCerebral Palsy and Movement Disorders
Canadian institutionsFonds de Recherche du Québec - SantéSante MontrealUniversité de Sherbrooke
Fundersnot available
KeywordsCerebral palsyChronic painMedicinePathophysiologyQuality of life (healthcare)BioinformaticsNeurosciencePsychologyPhysical therapyInternal medicine

Abstract

fetched live from OpenAlex

The high prevalence of chronic pain in individuals with cerebral palsy (CP) across the lifespan has been well documented, as has its negative impact on quality of life. However, without an understanding of the underlying (possibly unique) pathophysiology of pain in CP, identification of more effective management options, such as innovative and individualized pharmacological approaches to non-opioid pain treatment, will be significantly hindered. We review, briefly, what is known about chronic pain in CP and present what we need to know with respect to the neurobiology of pain and new developments in pain treatment research that might be applied to CP. WHAT THIS PAPER ADDS: Pain conditions in cerebral palsy have differing mechanisms and will not respond to the same treatments. Novel analgesics under development include inhibitors of ion channels, nerve growth factor, and calcitonin gene-related peptide.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0040.001

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 head0.108
GPT teacher head0.417
Teacher spread0.308 · 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
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

Citations57
Published2018
Admission routes1
Has abstractyes

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