Interactions- Readdressing the issue
Bibliographic record
Abstract
Broadly drugs include all the chemical substances excluding food that affect the bodily processes. The drug is considered to be a medicine if it benefits the body. Whereas, if the drug is injurious to the body, it’s considered as a poison. Therefore, the same chemical can be a boon or curse with respect to the situation, condition of use, dosage and the individual using it. In this contemporary healthcare era, a huge number of medications are formulated each year and new interactions between drugs are reported every now and then. As a result, it is no more practical for doctors to be dependent on the memory alone to avoid possible drug interactions. Changes in absorption, distribution, metabolism or elimination of drugs are referred to as pharmacokinetic interactions, resulting in alteration in the level of drugs and its metabolites. The effect of drug changes from person to person than expected because it causes different reaction when a drug reacts with the food or dietary supplements they take (drug -food interaction). So, the effect of the drug is altered by means of increasing, decreasing, or producing a new effect which cannot be produced on its own the effect caused by food or dietary supplements. These interactions may occur due to accidental misuse or due to other factors such as lack of knowledge about it. This review provides a comprehensive literature review on various drug interaction. Generally, drug food interactions are neglected and not well defined but it can cause mild to serious effects. However, all clinicians, pharmacists and nurses should be aware of drug interaction to avoid the consequences caused by drug interactions.
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 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.115 | 0.040 |
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".