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Record W2006545134 · doi:10.1136/ebn.8.4.107

Aspirin, 1000 mg, reduced moderate to severe pain in acute migraine headache

2005· letter· en· W2006545134 on OpenAlexaff
Diana E. McMillan

Bibliographic record

VenueEvidence-Based Nursing · 2005
Typeletter
Languageen
FieldMedicine
TopicMigraine and Headache Studies
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsMedicineMigraineAspirinPlaceboWeb of sciencePhonophobiaInternal medicineAura

Abstract

fetched live from OpenAlex

Lipton RB, Goldstein J, Baggish JS, et al . Aspirin is efficacious for the treatment of acute migraine. Headache 2005;45:283–92.[OpenUrl][1][CrossRef][2][PubMed][3][Web of Science][4] Q Is a single dose of aspirin, 1000 mg, effective for treatment of acute migraine with moderate to severe pain intensity? ### ![Graphic][5] Design: Randomised, placebo controlled trial. ### ![Graphic][6] Allocation: unclear concealment. ### ![Graphic][7] Blinding: blinded (patients). ### ![Graphic][8] Follow up period: several time points up to 24 hours. ### ![Graphic][9] Setting: USA. ### ![Graphic][10] Patients: 485 patients who were 18–50 years of age (mean age 37 y, 79% women, 77% white); had experienced migraine headache, with or without aura, according to International Headache Society (IHS) criteria; had at least moderate pain; and had ⩾1 but ⩽6 migraines per month during the previous year. Exclusion criteria included vomiting ⩾20% of the time during an attack; initiation of preventive medication in the past 3 months or use of alkaloids to treat migraine; use of anticoagulant, gout, or arthritis medications; and previous non-responsiveness … [1]: {openurl}?query=rft.jtitle%253DHeadache%26rft.stitle%253DHeadache%26rft.aulast%253DLipton%26rft.auinit1%253DR.%2BB.%26rft.volume%253D45%26rft.issue%253D4%26rft.spage%253D283%26rft.epage%253D292%26rft.atitle%253DAspirin%2Bis%2Befficacious%2Bfor%2Bthe%2Btreatment%2Bof%2Bacute%2Bmigraine.%26rft_id%253Dinfo%253Adoi%252F10.1111%252Fj.1526-4610.2005.05065.x%26rft_id%253Dinfo%253Apmid%252F15836564%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [2]: /lookup/external-ref?access_num=10.1111/j.1526-4610.2005.05065.x&link_type=DOI [3]: /lookup/external-ref?access_num=15836564&link_type=MED&atom=%2Febnurs%2F8%2F4%2F107.atom [4]: /lookup/external-ref?access_num=000228065000005&link_type=ISI [5]: /embed/inline-graphic-1.gif [6]: /embed/inline-graphic-2.gif [7]: /embed/inline-graphic-3.gif [8]: /embed/inline-graphic-4.gif [9]: /embed/inline-graphic-5.gif [10]: /embed/inline-graphic-6.gif

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.057
GPT teacher head0.339
Teacher spread0.282 · 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

Citations0
Published2005
Admission routes1
Has abstractyes

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