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Record W2339502098 · doi:10.1111/head.12823

The Evidence for Prescription Information for Possible Use of a Repeated Dose of Oral Triptans: A Comment

2016· article· en· W2339502098 on OpenAlexaboutno aff
Peer Tfelt‐Hansen, Stine F. Jespersen

Bibliographic record

VenueHeadache The Journal of Head and Face Pain · 2016
Typearticle
Languageen
FieldMedicine
TopicMigraine and Headache Studies
Canadian institutionsnot available
Fundersnot available
KeywordsTriptansDosingMedicineMedical prescriptionRandomized controlled trialMigraineAcute migraineIntensive care medicineAnesthesiaAlternative medicineSurgeryInternal medicinePharmacology

Abstract

fetched live from OpenAlex

A repeated dose of an oral triptan has been shown in randomized controlled trials (RCTs) to be ineffective as a treatment for no or partial response 2 hours after initial dosing but effective as treatment for headache recurrence. In the official prescription information in the United States, the United Kingdom, and Denmark repeated dosing of all seven oral triptans are recommended for headache recurrence but some triptans are also recommended for no or partial response, a use of a triptan deemed to be not beneficial in European and Canadian migraine guidelines. In summary, not all recommendations are based on evidence from RCTs. In addition, some recommendations are ambiguous and patients need clearer instructions. An example of such a clear British instruction in a patient leaflet instruction is presented at the end of the paper.

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)0.075
metaresearch head score (Gemma)0.372
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: Commentary · Consensus signal: Commentary
Teacher disagreement score0.075
Threshold uncertainty score0.396

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0750.372
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.008
Bibliometrics0.0020.003
Science and technology studies0.0040.009
Scholarly communication0.0060.013
Open science0.0080.004
Research integrity0.0630.057
Insufficient payload (model declined to judge)0.0190.010

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 head0.130
GPT teacher head0.358
Teacher spread0.227 · 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
GenreCommentary

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

Citations2
Published2016
Admission routes1
Has abstractyes

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