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Record W3004991292 · doi:10.1111/head.13767

The Profile and Prognosis of Youth With Status Migrainosus: Results From an Observational Study

2020· article· en· W3004991292 on OpenAlexaff
Serena L. Orr, Abigail Norris Turner, Marielle A. Kabbouche, Paul S. Horn, Hope L. O’Brien, Joanne Kacperski, Susan L. LeCates, Shannon White, Jessica Weberding, Mimi N. Miller, Scott W. Powers, Andrew D. Hershey

Bibliographic record

VenueHeadache The Journal of Head and Face Pain · 2020
Typearticle
Languageen
FieldMedicine
TopicMigraine and Headache Studies
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMedicineMigraineOdds ratioAnxietyPediatricsPopulationBody mass indexCohortConfidence intervalInternational Classification of Headache DisordersLogistic regressionRetrospective cohort studyPhysical therapyInternal medicinePsychiatry

Abstract

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Objective To characterize the clinical features of a large sample of children, adolescents, and young adults with a history of status migrainosus (SM) and to describe their short‐term prognosis. Background Data on the clinical characteristics of children and adolescents with SM are sparse and little is known about the prognosis of this population. Methods This was a retrospective clinical cohort study that included patients from the Cincinnati Children’s Headache Center if they had a diagnosis of migraine and data available for a 1‐3 months follow‐up interval. Data extracted from the initial interval visit (visit A) included: age, sex, race, migraine diagnosis, SM history, chronic migraine, medication overuse headache (MOH), body mass index (BMI), headache frequency, headache severity, disability, allodynia and lifestyle habits: caffeine intake, meal skipping, sleep duration, exercise frequency, and fluid intake. Data extracted from the initial consultation visit included: months with headache at initial consultation visit, patient endorsing “feeling depressed” and anxiety symptoms. Headache frequency and visit type were also measured at the second visit (visit B) in the follow‐up interval. A multivariate logistic regression model with a backward elimination procedure was created to model the odds of having a diagnosis of SM using the cross‐sectional predictor variables above. Second, chi‐square tests were used to compare the proportion of patients with SM to the proportion of patients without SM who had each of the following outcomes in the short‐term follow‐up window: treatment response (50% or greater reduction in headache frequency), overall reduction in headache frequency (reduction of 1 or more headache days/month), minimal change in headache frequency (increase in 0‐3 headache days/month), and clinical worsening (increase in 4 or more headache days/month). Results A total of 5316 youth with migraine were included and 559 (10.5%) had a history of SM. In the multivariate logistic regression model, predictors significantly associated with SM were: older age (OR = 1.13, 95% CI = 1.09‐1.17, P < .0001), migraine with aura (MWA) (OR = 1.30, 95% CI = 1.03‐1.65, P = .03), MOH (OR = 1.72, 95% CI = 1.30‐2.28, P = .0001), headache frequency (OR = 0.99, 95% CI = 0.97‐0.99, P = .030), higher headache severity (OR = 1.08, 95% CI = 1.02‐1.15, P = .009), months with headache at initial consultation (OR = 1.00, 95% CI = 1.00‐1.01, P = .042), and admission to infusion center at visit B (OR = 2.27, 95% CI = 1.38‐3.72, P = .001). Patients with a history of SM were more likely to experience an increase in 4 or more headache days per month at follow‐up: 15.2% as compared to 11.1% of those without SM, χ 2 (1, n = 5316) = 8.172, P = .0043. Conclusions Youth with SM represent a distinct subgroup of the migraine population and have an unfavorable short‐term prognosis.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.138
GPT teacher head0.333
Teacher spread0.195 · 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 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".

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Citations14
Published2020
Admission routes1
Has abstractyes

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