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Record W4402893625 · doi:10.1017/cjn.2024.300

Disparities in the Representation of Sex, Race and Ethnicity in Tension-Type Headache Clinical Trials

2024· article· en· W4402893625 on OpenAlexaffvenue
Brendan Tao, Chia‐Chen Tsai, Sina Marzoughi, Carmen Kalo, Jaden Lo, Arya Ebadi, Ana Marissa Lagman‐Bartolome, Faisal Khosa

Bibliographic record

VenueCanadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques · 2024
Typearticle
Languageen
FieldMedicine
TopicMigraine and Headache Studies
Canadian institutionsVancouver General HospitalWestern UniversityMcMaster UniversityLondon Health Sciences CentreChildren's Hospital of Western OntarioSickKids FoundationUniversity of TorontoHospital for Sick ChildrenUniversity of British Columbia
Fundersnot available
KeywordsEthnic groupGeneralizability theoryClinical trialRace (biology)MedicineDiseaseHealth equityClinical psychologyPsychologyDevelopmental psychologyPublic healthGender studiesInternal medicinePolitical sciencePathologySociology

Abstract

fetched live from OpenAlex

While tension-type headache (TTH) is the most common primary headache disorder, its effect according to sex, race and ethnicity remains unclear. We investigated disparities in sex, racial and ethnic representation in TTH clinical trials with comparison to global disease burdens. In this cross-sectional analysis, TTH clinical trials had female overrepresentation and racial and ethnic minority underrepresentation, which may affect understanding of the impact of TTH on different populations and personalized treatment development. Trial enrollment that is diverse and reflective of global disease burdens is crucial for improving study generalizability, understanding of diverse clinical presentations, and ensuring healthcare equity.

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)0.050
metaresearch head score (Gemma)0.139
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.950
Threshold uncertainty score0.267

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0500.139
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.227
GPT teacher head0.441
Teacher spread0.213 · 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.

Study designObservational
DomainMethods
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

Citations1
Published2024
Admission routes2
Has abstractyes

Explore more

Same venueCanadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques→Same topicMigraine and Headache Studies→French-language works237,207→