Update on the drug-food interactions
Bibliographic record
Abstract
Drug interactions are a major public health problem, which partly attributed to some 10,000 deaths/year in Canada. Besides the interactions between two drugs, drug interactions are also due to the effect of other substances such as foods or nutrients. The drug-food interaction will be pharmacokinetic (affecting the absorption, distribution, metabolism, and elimination) or pharmacodynamic interaction. It is in the intestine that food may have the greatest impact with mainly a change in the amount of drugs absorbed that may be clinically significant for some drugs with narrow therapeutic index (cyclosporine, phenytoin, theophylline, etc.). The absorption of the drug in the presence of food will be determined by the particular physicochemical properties of the drug but also by the impact of food on one of the parameters determining the absorption such as: modified gastric acidity and emptying, the fat content of the food, the use of common transport between the drug and nutrients, chemical reactions between elements and drugs. Fasting situations or malnutrition can affect the distribution of drugs by increasing the free drug fraction, involving sometimes the risk of overdose. Diet affects drug metabolism by changing the activity of cytochrome P450. Most often is described the increase by grapefruit juice (enzyme inhibitor) of plasma concentrations of some drugs (cyclosporine, some statins, and calcium antagonists). Other foods (garlic, smoked meats and fish, caffeine) may increase metabolism. Diet can influence two stages of renal clearance (glomerular filtration - tubular reabsorption) by modifying urine pH or renal clearance. Pharmacodynamic interactions are also monitored, especially foods rich in vitamin k or tyramine with antivitamins K or MAOIs. Finally, health professionals must mobilize against these interactions, including through patient information.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.018 | 0.008 |
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".