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Record W4415912456 · doi:10.1111/head.15093

Chinook winds and migraine attack onset in children and adolescents: A prospective longitudinal clinical cohort study

2025· article· en· W4415912456 on OpenAlexafffundabout
Rylan Heart Villaruz, Jonathan Kuziek, Kirsten Sjonnesen, Lindsay Craddock, Werner J. Becker, Ashley D. Harris, Serena L. Orr

Bibliographic record

VenueHeadache The Journal of Head and Face Pain · 2025
Typearticle
Languageen
FieldMedicine
TopicMigraine and Headache Studies
Canadian institutionsAlberta Children's HospitalUniversity of Calgary
FundersAllerganH. Lundbeck A/SUniversity of CambridgeAlberta Children's Hospital Research InstituteAmerican Headache SocietyCanadian Institutes of Health ResearchTeva Pharmaceutical IndustriesPfizerUniversité de SherbrookeAlberta InnovatesUniversity of Calgary
KeywordsMigraineCohort studyLongitudinal studyCohortProspective cohort studyAssociation (psychology)Statistical analysis

Abstract

fetched live from OpenAlex

OBJECTIVE: To explore the relationship between migraine attack onset in children and adolescents and Chinooks, which are dry and warm westerly winds that generally occur in the winter and bring about abrupt weather changes to the east of the Rocky Mountains in Southern Alberta, Canada. METHODS: This was a prospective longitudinal clinical cohort study with recruitment from November 2020 to May 2024. Participants were: 8-18 years old, had migraine as per International Classification of Headache Disorders 3rd edition criteria, had 1-15 headache days/month, lived in the geographical location where Chinook winds occur, and had exposure to at least one pre-Chinook or Chinook day during the study period. Chinook days were defined using Nkemdirim's criteria and Environment Canada data were used to categorize day type as either Chinook, pre-Chinook, or non-Chinook. Weather data were merged with data from daily headache diaries, completed for periods of 8-30 days. The primary outcome was attack onset, defined as a day with a new migraine attack of moderate or severe severity, as per the 4-point scale (0 = none, 1 = mild, 2 = moderate, and 3 = severe). Both univariate and adjusted models were used to determine if there was an association between migraine attack onset and day type (i.e., pre-Chinook, Chinook, or non-Chinook) at the aggregate study sample level. The adjusted models controlled for age and sex, and both models included a random intercept. Subsequently, individual n = 1 models were fitted to explore each individual participant's personal odds of migraine attack onset on both pre-Chinook and Chinook days versus non-Chinook days. Pre-Chinook/Chinook sensitivity values were calculated for each individual by dividing the model's regression coefficient by its standard error. Sensitivity values >1.96 suggest a significant association between pre-Chinook/Chinook days and attack onset. RESULTS: Sixty youth with 1253 days of complete data, of which 144 (12%) were attack onset days, participated in the study. There were 158 Chinook (13%), 124 pre-Chinook (10%), and 971 non-Chinook days (77%). There were 39 female participants (39 of 60; 65%), with a median age of 14 years (quartile [Q] 1 = 12, Q3 = 16), and a median headache frequency of 6.2 days/month (Q1 = 4, Q3 = 11). Neither the univariate nor the adjusted models found any significant association between day type and attack onset at an aggregate level (pre-Chinook adjusted odds ratio [OR], 0.98; 95% confidence interval [CI], 0.54-1.78, p = 0.947; Chinook adjusted OR, 1.15; 95% CI, 0.69-1.91, p = 0.596). No individual participants met the threshold for statistically significant pre-Chinook or Chinook sensitivity. CONCLUSION: We did not find a relationship between pre-Chinook and Chinook conditions and migraine attack onset. This may be due to the lack of an association between Chinooks and attack onset in youth with migraine, or due to a lack of statistical power in our study. Future studies with greater statistical power should aim to assess for a potential relationship between Chinooks and attack onset, as it could have important treatment implications.

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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)0.001
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 score0.077
Threshold uncertainty score0.152

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.022
GPT teacher head0.356
Teacher spread0.333 · 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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Citations0
Published2025
Admission routes3
Has abstractyes

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