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Record W3153370700 · doi:10.1177/08830738211007685

Sensory Function and Psychological Factors in Children With Complex Regional Pain Syndrome Type 1

2021· article· en· W3153370700 on OpenAlexaff
Emma E. Truffyn, Massieh Moayedi, Stephen C. Brown, Danielle Ruskin, Emma G. Duerden

Bibliographic record

VenueJournal of Child Neurology · 2021
Typearticle
Languageen
FieldMedicine
TopicPain Management and Treatment
Canadian institutionsChildren’s Health Research InstituteHospital for Sick ChildrenUniversity of TorontoSickKids FoundationWestern University
Fundersnot available
KeywordsComplex regional pain syndromePsychosocialAnxietyNociceptionForearmSensationSensory systemQuantitative sensory testingPsychologyPain catastrophizingHyperalgesiaPhysical therapyChronic painMedicineThreshold of painAnesthesiaPsychiatryInternal medicineNeuroscienceSurgery

Abstract

fetched live from OpenAlex

Objective: To assess thermal-sensory thresholds and psychosocial factors in children with Complex Regional Pain Syndrome Type 1 (CRPS-I) compared to healthy children. Methods: We conducted quantitative sensory testing on 34 children with CRPS-I and 56 pain-free children. Warm, cool, heat, and cold stimuli were applied to the forearm. Children with CRPS-I had the protocol administered to the pain site and the contralateral-pain site. Participants completed the self-report Behavior Assessment System for Children. Results: Longer pain durations (>5.1 months) were associated with decreased sensitivity to cold pain on the pain site ( P = .04). Higher pain-intensity ratings were associated with elevated anxiety scores ( P = .03). Anxiety and social stress were associated with warmth sensitivity (both P < .05) on the contralateral-pain site. Conclusions: Pain duration is an important factor in assessing pediatric CRPS-I. Hyposensitivity in the affected limb may emerge due to degeneration of nociceptive nerves. Anxiety may contribute to thermal-sensory perception in childhood CRPS-I.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.236

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.045
GPT teacher head0.275
Teacher spread0.230 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations7
Published2021
Admission routes1
Has abstractyes

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