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Record W4386750949 · doi:10.3389/fnins.2023.1235945

Interim guidelines for the assessment and treatment of pain in children with multiple sclerosis

2023· article· en· W4386750949 on OpenAlexaff
Catherine Stratton, Areti Vassilopoulos, J. Nicholas Brenton, Kirsten Potter, Wendy Vargas, Heather Rumm, Andrea Bartels, Mary A. Bailey, Charles A. Odonkor, Sharon Stoll, E. William T. Zempsky, E. Ann Yeh, Naila Makhani

Bibliographic record

VenueFrontiers in Neuroscience · 2023
Typearticle
Languageen
FieldMedicine
TopicPediatric Pain Management Techniques
Canadian institutionsSickKids FoundationPublic Health OntarioUniversity of Toronto
FundersNational Institute of Neurological Disorders and StrokeNational Institutes of HealthCharles H. Hood Foundation
KeywordsInterimGuidelinePhysical therapyDelphi methodMedicinePain assessmentMultiple sclerosisPain managementMEDLINEPsychologyPsychiatry

Abstract

fetched live from OpenAlex

Introduction: Pain in multiple sclerosis (MS) is common, but literature on pain in children with MS remains scarce. Pain has physical, psychological, and social implications in MS, and both comprehensive assessment and interdisciplinary management approaches are needed. We sought to develop an interdisciplinary interim guideline for the assessment and management of pain in children with MS. Methods and materials: We convened a modified Delphi panel composed of 13 experts in pediatric and adult MS neurology, physiotherapy, pain, patient lived-experience, advanced practice nursing, psychology, physiatry, and MS research. A survey was sent to panelists for anonymous completion. The panel discussed survey themes extracted by the panel chair. The process was repeated twice. Results: Thirteen assessment and treatment recommendations were produced regarding pain in children with MS. Discussion: Future studies will assess implementation of these pain assessment and treatment guidelines in the clinical setting.

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)0.034
metaresearch head score (Gemma)0.061
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: Not applicable
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.034
Threshold uncertainty score0.178

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0340.061
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.003
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0030.003
Research integrity0.0070.008
Insufficient payload (model declined to judge)0.0050.004

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 head0.090
GPT teacher head0.346
Teacher spread0.255 · 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
GenreMethods

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

Citations5
Published2023
Admission routes1
Has abstractyes

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