Healthcare Workers and Antineoplastic Drugs: Identifying the Determinants of Exposure and Current Challenges to Reducing Exposure
Bibliographic record
Abstract
This research aimed to identify the possible determinants of antineoplastic (anti-cancer) drug exposure, in order to develop an evidence-based approach to minimize the exposure risk. The researchers explored which factors are potential barriers to compliance with safe work procedures in order to facilitate changes in practices/attitudes This study was aimed at better understanding the effectiveness of cleaning protocols in work environments where antineoplastic (anti-cancer) drugs are handled and prepared. A variety of health care workers, besides pharmacy personnel and drug administering nurses, are at risk of occupational exposure to antineoplastic drugs, which can have carcinogenic effects on healthy cells. The researchers calculated a risk estimate based on average contamination levels measured in research participants’ urine, and discovered that there was a slight increase in risk of cancer for health care workers who had absorbed the antineoplastic drug cyclophosphamide (CP), the drug used as the marker in this study Based on these results, the researchers suggest that current protocols for eliminating surface contamination are not as effective as intended, and make recommendations for improving control and handling procedures to minimize worker exposure to these substances
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 | 0.009 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".