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Record W2157350479 · doi:10.2147/ppa.s6152

Rizatriptan for the acute treatment of migraine: Consistency, preference, satisfaction, and quality of life

2009· article· en· W2157350479 on OpenAlexaffabout
Pringsheim

Bibliographic record

VenuePatient Preference and Adherence · 2009
Typearticle
Languageen
FieldMedicine
TopicMigraine and Headache Studies
Canadian institutionsAlberta Children's HospitalUniversity of Calgary
Fundersnot available
KeywordsRizatriptanMedicineTriptansMigrainePlaceboClinical trialHeadachesAnesthesiaSumatriptanIntensive care medicineInternal medicineAlternative medicineAgonistSurgery

Abstract

fetched live from OpenAlex

Rizatriptan for the acute treatment of migraine: Consistency, preference, satisfaction, and quality of life Farnaz Amoozegar, Tamara PringsheimCalgary Headache Assessment and Management Program, Department of Clinical Neurosciences, University of Calgary, Calgary, AB, CanadaAbstract: Rizatriptan is a 5HT (IB/ID) agonist with proven efficacy in the acute treatment of migraine headache. We performed a systematic review of the literature for clinical trials of rizatriptan incorporating important patient outcomes including consistency of response, preference, satisfaction, and quality of life. We found evidence that rizatriptan provides consistent relief of migraine attacks and that patients prefer rizatriptan over other treatments because of its speed of relief. Patient satisfaction with rizatriptan is significantly higher than placebo, but appears equivalent to most other triptans. Migraine-specific quality of life at 24 hours is significantly better in patients treated with rizatriptan compared to placebo, while overall long-term quality of life is less affected. The published clinical trials included in this systematic review are subject to bias due to the open-label nature of preference trials and the doses chosen for comparison in head-to-head trials.Keywords: migraine, rizatriptan, patient preference

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.204
Threshold uncertainty score0.413

Codex and Gemma teacher scores by category

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

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 head0.143
GPT teacher head0.336
Teacher spread0.193 · 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 teacher head, 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

Citations10
Published2009
Admission routes2
Has abstractyes

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