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Record W4250232779 · doi:10.2310/em.4174

Headache

2018· article· en· W4250232779 on OpenAlexaboutno aff
Benjamin W. Friedman

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicMigraine and Headache Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineHeadachesMigraineCluster headacheReversible cerebral vasoconstriction syndromeSumatriptanDifferential diagnosisPediatricsSubarachnoid hemorrhageAnesthesiaSurgeryInternal medicinePathology

Abstract

fetched live from OpenAlex

Headaches are one of the most common complaints of patients seen by emergency physicians. They can be classified as primary headaches, which have no identifiable underlying cause, and secondary headaches, which are classified according to their cause. The majority of headaches are benign in origin, and most patients with headache can be treated successfully in the emergency department and discharged home; however, some have potentially life-threatening causes, and consideration of a broad differential diagnosis for all patients is essential. This review covers the primary headache disorders, pathophysiology, stabilization and assessment, diagnosis and treatment, and disposition and outcomes. The figure shows areas of the brain sensitive to pain. Tables review differential diagnosis of headache, International Headache Society primary headache criteria, clinical characteristics of secondary headaches, high-risk clinical characteristics among patients with a headache peaking in intensity within 1 hour, drugs associated with headache, and parenteral treatment of acute migraine. This review contains 1 figure, 9 tables, and 58 references. Key words: migraine, calcitonin gene related peptide, greater occipital nerve block, venous sinus thrombosis, reversible cerebral vasoconstriction syndrome, Ottawa, subarachnoid, cluster headache, trigeminal autonomic cephalalgias, post-traumatic headache

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)0.002
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: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.135
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.1350.062

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 head0.041
GPT teacher head0.344
Teacher spread0.303 · 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
GenreOther

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

Citations0
Published2018
Admission routes1
Has abstractyes

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