Bibliographic record
Abstract
Effects of other drugs on eletriptan CYP3A4 inhibitors: In fits studies have shown that eletriptan is metabolized if the CYP3A4 enzyme.Ketocotiazole: A clinical study demonstrated about a 3-fold increase in Cra, and about a 6-fold increase in the AUC of eletriptan when coadministered with ketrxonazole.The half-life of eletriptan increased from 5 h to 8 h and the T " increased from 2.8 h to 5.4 h.Erythromycin: A clinical study demonstrated about a 2-fold increase in eletriptan C " and about a 4-fold increase in AUC when erythromycin was co-administered with eletriptan.This increased exposure was associated with an increase in eletriptan half-life from 4.6 h to 7.1 h.Fluconazole: Co-administration of fluconazole and eletriptan yields about a 1.4-fold increase in Cma and about a 2-fold increase in AUC of eletriptan.Verapamil: It has also been shown that co-administration of verapamil and eletriptan yields about a 2-fold increase in C " and about a 3-fold increase in AUC of eletriptan.Propranolol: The C^, and AUC of eletrifen were roeased by 10% and 33%, respectively, foilewig an 80 mg BSD dose of propranolol administered for 7 days.No Weracftre increases in blood pressure were observed.No dose aolustmenf is necessary for patients afeo taking propramW.MAO inhibitors: Eletriptan Is not a substrate for monoamine oxidase (MAO) enzymes.Therefore there is no expectation of an interaction between RELPAX and MAO inhibitors. The effect of eletriptan on other drugs
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.872 | 0.772 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".