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Record W120083802 · doi:10.1017/s0317167100051428

Chronic Daily Headache in Children and Adolescents: A Multi-Faceted Syndrome

2010· review· en· W120083802 on OpenAlexaffvenue
Shashi S. Seshia, Shuu‐Jiun Wang, Ishaq Abu‐Arafeh, Andrew D. Hershey, Vincenzo Guidetti, Paul Winner, Christian Wöber

Bibliographic record

VenueCanadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques · 2010
Typereview
Languageen
FieldMedicine
TopicMigraine and Headache Studies
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsMedicineBiopsychosocial modelChronic MigraineChronic painAnxietyMigrainePhysical therapyPsychiatryPediatrics

Abstract

fetched live from OpenAlex

Chronic daily headache (CDH) is a multi-faceted, often complex pain syndrome in children and adolescents. Chronic daily headache may be primary or secondary. Chronic migraine and chronic tension-type are the most frequent subtypes. Chronic daily headache is co-morbid with adverse life events, anxiety and depressive disorders, possibly with other psychiatric disorders, other pain syndromes and sleep disorders; these conditions contribute to initiating and maintaining CDH. Hence, early management of episodic headache and treatment of associated conditions are crucial to prevention. There is evidence for the benefit of psychological therapies, principally relaxation and cognitive behavioral, and promising information on acupuncture for CDH. Data on drug treatment are based primarily on open label studies. The controversies surrounding CDH are discussed and proposals for improvement presented. The multifaceted nature of CDH makes it a good candidate for a multi-axial classification system. Such an approach should facilitate biopsychosocial management and enhance consistency in clinical research.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.004
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.052
GPT teacher head0.323
Teacher spread0.271 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations41
Published2010
Admission routes2
Has abstractyes

Explore more

Same venueCanadian Journal of Neurological Sciences / Journal Canadien des Sciences NeurologiquesSame topicMigraine and Headache StudiesFrench-language works237,207