Bibliographic record
Abstract
A recent article offers a useful summary of steps to consider concerning drug–drug interactions during drug development [1]. However, the article focused mainly on anticipating pharmacokinetic and drug metabolism-based interactions. The more important issue is that while in some cases such changes can anticipate potentially clinically important interactions, for example, with drugs acting directly on peripheral cardiovascular receptors or having a small volume of distribution, for many classes of drugs such studies cannot usefully predict clinically important interactions in individual patients [2, 3]. The reasons are mainly because of the large variation among patients in the extent of kinetic and metabolism changes combined with much larger variation in pharmacodynamic and therapeutic effects. Preoccupation with kinetic and metabolic-based interactions mechanisms has occurred because these are more easily studied than the more important pharmacodynamic consequences in individual patients. Population (average) changes in kinetics, metabolites and pharmacodynamics are not useful in actual clinical dose adjustment. This shortcoming is compounded by the relative lack of clinical studies in actual conditions and settings of drug use and in neglected study populations including different ancestry groups, low socioeconomic groups and individuals with restricted access to health care services. As a result of being understudied, much clinical use of drugs and management of drug–drug interactions is largely trial and error. Many more “real” world post-marketing studies of drug–drug interactions are needed. As final comment, these authors elect to distinguish the drugs involved in drug–drug interactions as the “perpetrator” and the “victim.” This is a useful categorical and semantic shortcut but belies the complexity of the etiology and complexity of drug–drug interactions where often multiple drugs are administered concurrently in disease states that modify mechanism and consequences. In clinical practice, the perpetrator is often the prescriber and the victim the patient! The author has nothing to report. The author declares no conflicts of interest.
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 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 | 0.002 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".