Response to ‘Aprepitant and fosaprepitant decrease the effectiveness of hormonal contraceptives’
Bibliographic record
Abstract
We thank Bailard and Rebello 1 for bringing attention to the clinical relevance of the interaction between aprepitant/fosaprepitant and ethinyl estradiol. Publications were included in our systematic review 2 if they (1) described changes in pharmacokinetic parameters of a drug given concomitantly with aprepitant or fosaprepitant or described an adverse event ascribed to a drug interaction with aprepitant or fosaprepitant; (2) described these events in humans; (3) reported primary data; and (4) were published in full-text or, for meeting abstracts, were published in 2013 or later. Since a description of this interaction has not been formally published, we were unable to include it in our systematic review. We are in agreement with Bailard and Rebello. The interaction between ethinyl estradiol and aprepitant/fosaprepitant is likely clinically significant and an important interaction to take into consideration in clinical practice. This example signals to researchers and pharmaceutical companies the importance of publishing drug interaction study data fully and openly using methods outlined in the United States Food and Drug Administration guidance document 3 for conducting drug interaction studies. Having the full details of drug-drug interaction study findings openly available increases awareness of clinically significant interactions and enables healthcare providers to make informed clinical decisions. There are no competing interests to declare.
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 | 0.005 | 0.067 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.017 | 0.010 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".