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Record W2606966847 · doi:10.1177/0883073817702025

Disability, Quality of Life, and Pain Coping in Pediatric Migraine: An Observational Study

2017· article· en· W2606966847 on OpenAlexaff
Serena L. Orr, Suzanne Christie, Salwa Akiki, Hugh J. McMillan

Bibliographic record

VenueJournal of Child Neurology · 2017
Typearticle
Languageen
FieldMedicine
TopicPediatric Pain Management Techniques
Canadian institutionsOttawa HospitalChildren's Hospital of Eastern OntarioUniversity of Ottawa
FundersAllerganAmerican Headache SocietyBiogen
KeywordsPain catastrophizingMigraineMedicineCoping (psychology)Physical therapyObservational studyLogistic regressionQuality of life (healthcare)NeurologyClinical psychologyPsychiatryChronic painInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVES: The objective was to examine the relationship between disability, health-related quality of life (HrQoL), and pain coping in pediatric migraineurs. METHOD: Eighty-five patients with migraine were recruited from Pediatric Neurology clinics. Participants completed the Pediatric Migraine Disability Assessment Scale, the Pediatric Quality of Life Inventory, the Pain Coping Questionnaire, and the Pain Catastrophizing Scale. Means were compared to published norms using t-tests. Spearman correlations and logistic regression were used to explore the relationships between the variables. RESULTS: Mean HrQoL scores were lower than norms for controls and chronically ill pediatric patients ( P < .0001). Patients reported lower mean pain coping scores and higher mean pain catastrophizing scores than norms ( P < .0001). After controlling for age and sex, only the relationship between disability and HrQoL remained significant (OR = 0.91, 95% CI: 0.86-0.95). CONCLUSION: Pediatric patients with migraine report lower HrQoL, fewer pain coping strategies and more catastrophizing than controls, while disability is inversely associated with HrQoL.

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)0.005
metaresearch head score (Gemma)0.005
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 score0.037
Threshold uncertainty score0.628

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.005
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 head0.110
GPT teacher head0.389
Teacher spread0.278 · 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

Citations38
Published2017
Admission routes1
Has abstractyes

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