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Record W4403035585 · doi:10.3899/jrheum.2024-0875

Stop Interfering! A Reflection on the Intrusive and Elusive Nature of Pain in Juvenile Idiopathic Arthritis

2024· editorial· en· W4403035585 on OpenAlexaffvenueabout
Tara McGrath, Dax G. Rumsey

Bibliographic record

VenueThe Journal of Rheumatology · 2024
Typeeditorial
Languageen
FieldMedicine
TopicAutoimmune and Inflammatory Disorders Research
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMedicineJuvenileArthritisPhysical therapyInternal medicine

Abstract

fetched live from OpenAlex

The face of juvenile idiopathic arthritis (JIA) management has transformed over the last 30 years, with advancements that have dramatically improved outcomes and overall prognosis. Yet, despite contemporary advances, including biologic therapies, pain in patients with JIA remains common.1 Pain has been reported as the most distressing manifestation of JIA.2 Why is it that we have come so far in JIA treatment but effective pain management for a subset of our patients with JIA remains elusive? The multifaceted and complex nature of pain in JIA makes it difficult to quantify and, therefore, difficult to study. Despite this, researchers in recent years have continued to work to determine the best way to measure, interpret, and understand pain in patients with JIA. Traditional pain measures that continue to be used in clinical settings have marked limitations. The 100-mm (or 10-cm) visual analog scale (VAS), which has been a foothold for both clinical and research measurement of pain intensity in patients with JIA, is the prototype of single-dimensional pain measurement tools. This simple horizontal line is anchored at each end: at the farthest left with “no pain” and at the farthest right with “worst pain imaginable.” The VAS has been validated in pediatric populations and is used as both a self-reported and proxy-reported measure of pain intensity.3 This has been followed by numerous similar single-dimensional tools including various facial scales (eg, Oucher Scale, Wong-Baker FACES Pain Rating Scale, Faces Pain Scale), body outline figures (eg, body map), and other scales such as the Varni/Thompson Pediatric Pain Questionnaire that combine several instruments (VAS, body map, and a list of pain descriptors).4 Though these and similar adapted pain scales have been deemed adequate to measure pain intensity, a single measurement in a clinical setting is vastly insufficient at capturing the full … Address correspondence to Dr. T.R. McGrath, Department of Pediatrics, Division of Rheumatology, University of Alberta, 3‑508 ECHA, 11405 87 Ave NW, Edmonton, AB T6G 1C9, Canada. Email: trm{at}ualberta.ca.

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)0.007
metaresearch head score (Gemma)0.022
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: Editorial · Consensus signal: none
Teacher disagreement score0.012
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.022
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0040.011
Scholarly communication0.0050.012
Open science0.0030.003
Research integrity0.0120.037
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 head0.010
GPT teacher head0.296
Teacher spread0.286 · 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
GenreEditorial

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

Citations0
Published2024
Admission routes3
Has abstractyes

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