Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Clinically significant grapefruit juice-drug interactions are an interesting development in the last ten years of research process in the scope of drug interactions. In 1989 a group of Canadian researchers found incidentally that grapefruit juice, used as a carrier system, interacts with some calcium channel blockers, while applying alcohol during a study on alcohol-drug interactions, and presented it in Lancet as a "short report". In this report it is stated that this effect of grapefruit juice is specific and there is no similar interaction with orange juice. The grapefruit juice interactions with drugs and changes in drug pharmacokinetics, individual responses to grapefruit juice in the relationship between the drug concentration and the effect began to gain a larger clinical significance. Drugs interacting with grapefruit juice are metabolized by cytochrome P450 enzyme system in liver or intestinal section. Flavonoids contained in grapefruit juice inhibit the enzyme, bind as a substrate to the enzyme system and disrupt its bioavailability. Naringin is an essential bioflavonoid in the grapefruit juice. Naringin is not a potent inhibitor of cytochrome P450, but it is partly metabolised to "Naringenin" by intestinal bacteria. This substance is a powerful inhibitor of cytochrome P450 and it is believed by some researchers, that these are components responsible for the effect of grapefruit juice. In 2008 the number of drugs that can cause danger when taken together with grapefruit was 17. It has been reported that this number has risen to 43 in 2012. However, while the research of the possible effects of other not yet identified components of the grapefruit juice is still in progress, FDA has begun to put cautionary statement to many drug prescriptions.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it