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Record W4399922784 · doi:10.1111/head.14753

Characterizing neck pain during headache among people with migraine: Multicountry results from the Chronic Migraine Epidemiology and Outcomes – International (CaMEO‐I) cross‐sectional study

2024· article· en· W4399922784 on OpenAlexaboutno aff
Manjit Matharu, Zaza Katsarava, Dawn C. Buse, Katherine Sommer, Michael L. Reed, Kristina M. Fanning, Richard B. Lipton

Bibliographic record

VenueHeadache The Journal of Head and Face Pain · 2024
Typearticle
Languageen
FieldMedicine
TopicMigraine and Headache Studies
Canadian institutionsnot available
FundersAbbVie
KeywordsMigraineMedicineInternational Classification of Headache DisordersEpidemiologyNeck painCross-sectional studyChronic MigrainePhysical therapyPediatricsInternal medicineAlternative medicinePathology

Abstract

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OBJECTIVE: To assess the prevalence and impact of neck pain during headache among respondents with migraine in the multicountry Chronic Migraine Epidemiology and Outcomes - International (CaMEO-I) Study. BACKGROUND: Neck pain among individuals with migraine is highly prevalent and contributes to disability. METHODS: The CaMEO-I was a prospective, cross-sectional, web-based study conducted in Canada, France, Germany, Japan, United Kingdom, and the United States. A demographically representative sample of participants from each country completed a screening survey to evaluate headache characteristics. Respondents with headache were identified as having migraine or non-migraine headache based on modified International Classification of Headache Disorders, third edition, criteria; those with migraine completed a detailed survey with migraine-specific assessments. Results were stratified by the presence or absence of neck pain with headache (NPWH). For these analyses, data were pooled across the six countries. RESULTS: Of 51,969 respondents who reported headache within the past 12 months, 14,492 (27.9%) were classified as having migraine; the remaining 37,477 (72.1%) had non-migraine headache. Overall, 9896/14,492 (68.3%) of respondents with migraine headache reported NPWH, which was significantly higher (p < 0.001) than the proportion of respondents with non-migraine headache who reported NPWH (13,536/37,477 [36.1%]). Among respondents with migraine, moderate-to-severe disability was significantly more prevalent for those with NPWH versus without (47.7% [4718/9896] vs. 28.9%, p < 0.001). Respondents with NPWH versus without also had significantly greater work productivity losses, at a median (interquartile range [IQR]) of 50.0 (20.0, 71.3) vs. 30.0 (0.0, 60.0) (p < 0.001), lower quality of life (Migraine-Specific Quality of Life questionnaire version 2.1, median [IQR] Role Function-Restrictive domain score 60.0 [42.9, 74.3] vs. 68.6 [54.3, 82.9], p < 0.001), higher prevalence of depression and anxiety symptoms (depression, 40.2% [3982/9896] vs. 28.2% [1296/4596], p < 0.001); anxiety, 41.2% [4082/9896] vs. 29.2% [1343/4596], p < 0.001), higher prevalence of cutaneous allodynia during headache (54.0% [5345/9896] vs. 36.6% [1681/4596], p < 0.001), and higher prevalence of poor acute treatment optimization (61.1% [5582/9129] vs. 53.3% [2197/4122], p < 0.001). CONCLUSIONS: Nearly 70% of respondents with migraine reported NPWH. Individuals with migraine with neck pain during their headaches had greater disability, depression, anxiety, and cutaneous allodynia (during headache) than those without neck pain during their headaches. They also had diminished quality of life and work productivity, and poorer response to acute treatment compared with those without neck pain.

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)0.002
metaresearch head score (Gemma)0.004
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 score0.020
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.026
GPT teacher head0.321
Teacher spread0.294 · 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".

Quick stats

Citations15
Published2024
Admission routes1
Has abstractyes

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