The environmental impact of pharmaceuticals in Italy: Integrating healthcare and eco‐toxicological data to assess and potentially mitigate their diffusion to water supplies
Bibliographic record
Abstract
Pharmaceuticals can reach the environment at all stages of their lifecycle and accumulate in the ecosystem, potentially reaching toxic levels for animals and plants. In recent years, efforts have been made to map and control this hazard. Assessing country-specific environmental risks could drive regulatory actions towards eco-friendlier drug utilization and disposal practices. By starting from a list of 25 environmentally hazardous pharmaceuticals developed by Region Stockholm, we integrated eco-toxicological and 2019-2021 Italian drug utilization data to estimate the environmental impact of pharmaceuticals in Italy. We calculated the risk as the ratio between the predicted environmental concentration (PEC) and the predicted no-effect concentration (PNEC). We found a high risk for levonorgestrel, ciprofloxacin, amoxicillin, azithromycin, venlafaxine, sertraline and diclofenac and a moderate risk for ethinyloestradiol, oestradiol and clarithromycin. This analysis can be periodically performed to identify the pharmaceuticals with the highest risk for the environment and ascertain if containment measures should be implemented.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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 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".