Pharmaceuticals in the environment: scientific evidence of risks and its regulation
Bibliographic record
Abstract
During the past two decades scientists, regulatory agencies and the European Commission have acknowledged pharmaceuticals to be an emerging environmental problem. In parallel, a regulatory framework for environmental risk assessment (ERA) of pharmaceutical products has been developed. Since the regulatory guidelines came into force the German Federal Agency (UBA) has been evaluating ERAs for human and veterinary pharmaceutical products before they are marketed. The results show that approximately 10% of pharmaceutical products are of note regarding their potential environmental risk. For human medicinal products, hormones, antibiotics, analgesics, antidepressants and antineoplastics indicated an environmental risk. For veterinary products, hormones, antibiotics and parasiticides were most often discussed as being environmentally relevant. These results are in good correlation with the results within the open scientific literature of prioritization approaches for pharmaceuticals in the environment. UBA results revealed that prospective approaches, such as ERA of pharmaceuticals, play an important role in minimizing problems caused by pharmaceuticals in the environment. However, the regulatory ERA framework could be improved by (i) inclusion of the environment in the risk-benefit analysis for human pharmaceuticals, (ii) improvement of risk management options, (iii) generation of data on existing pharmaceuticals, and (iv) improving the availability of ERA data. In addition, more general and integrative steps of regulation, legislation and research have been developed and are presented in this article. In order to minimize the quantity of pharmaceuticals in the environment these should aim to (i) improve the existing legislation for pharmaceuticals, (ii) prioritize pharmaceuticals in the environment and (iii) improve the availability and collection of pharmaceutical data.
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 | 0.018 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".