ADVERSE DRUG REACTIONS: AN OVERVIEW OF THE OLD AND CONTINUOUS CHALLENGE TO DRUG THERAPY AND DEVELOPMENT
Bibliographic record
Abstract
Adverse drug reactions (ADRs) represent a major health problem worldwide and constitute a big challengeto drug therapy and the drug development process. ADRs are responsible for 3% of total hospital admissions and occur in 10 to 20% of hospitalized patients. It has been estimated that ADRs account for at least 100,000 deaths annually in the United States alone ranking them as the fifth leading cause of death. According to the World Health Organization definition an ADR is a noxious and unintended response to a drug that occurs at a dose normally used in man for prophylaxis, diagnosis or therapy. This commonly used definition, however, excludes other drug therapy consequences such as drug abuse, accidental and inadvertent drug overdose and therapeutic failure. ADRs are classified into two main groups: Type A, which are predictable from the drugs’ normal pharmacological actions and are dose dependent and Type B, which are unpredictable, unrelated to the drugs’ pharmacology and do not have clear dose dependency. This is an overview of the currently used definitions and classifications of ADRs in clinical pharmacology and toxicology. Specific relevant examples are cited and some important points are discussed in the light of current knowledge. A special emphasis is made on the importance of ADRs in clinical drug therapy and drug development, which are the areas where ADRs play the most significant role.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".