MétaCan
Menu
Back to cohort

Improved migraine management in primary care: results of a patient treatment experience study using zolmitriptan orally disintegrating tablet

2006· article· en· W1795204867 on OpenAlexaff
Gary Shapero, Andrew Dowson, Jean-Pierre Lacoste, Per Almqvist

Bibliographic record

VenueInternational Journal of Clinical Practice · 2006
Typearticle
Languageen
FieldMedicine
TopicMigraine and Headache Studies
Canadian institutionsCustom Security Industries (Canada)
Fundersnot available
KeywordsZolmitriptanMedicineTriptansMigrainePrimary careClinical endpointIntensive care medicineClinical trialAnesthesiaSumatriptanFamily medicineInternal medicine

Abstract

fetched live from OpenAlex

The 'Zomig Appropriate for Primary care' programme was developed to address the needs of primary care physicians (PCPs) to improve migraine management. As part of the programme, an international, open-label, 6-month clinical study was performed. The study included new and tangible outcome variables relevant to PCPs and recruited patients presenting in primary care with an established migraine diagnosis. Patients treated up to three migraine attacks per month with zolmitriptan orally disintegrating tablet (ODT) 2.5 mg. All other migraine attacks occurring during the study period were treated with the patient's usual migraine medication (including other triptans). Questionnaires were used to record patient treatment experiences at the study end. The primary end-point was the proportion of patients wanting to continue using zolmitriptan ODT. Some 595 patients treated 7171 migraine attacks with zolmitriptan ODT. Of the 504 patients who completed the 6-month questionnaire, 380 (75.4%) wished to continue using zolmitriptan ODT. The results of the study indicate that patient-orientated end-points are more motivational and meaningful to physicians than traditional end-points used in controlled clinical trials, allowing them to make informed decisions regarding migraine management.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.288
Threshold uncertainty score0.462

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.066
GPT teacher head0.453
Teacher spread0.387 · 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

Citations11
Published2006
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Clinical PracticeSame topicMigraine and Headache StudiesFrench-language works237,207