Healthcare workers’ exposure to aerosolized medications while crushing oral tablets
Bibliographic record
Abstract
Crushing oral tablets can potentially aerosolize active ingredients in the medication and expose healthcare workers to drug particulates. Few studies have quantified aerosolized particulate matter generated during tablet crushing. Inhalation of patient medications can result in negative health effects to the healthcare worker, especially if hazardous medications are being crushed. This study evaluated four different pill crusher and pill container combinations to assess particulate exposure risks and examine whether particulate levels varied depending on the pill crusher, container type, and crushing method. The pill crushers included MAXCRUSH, Silent Knight, and SafeCrush. The MAXCRUSH pill crusher was used with paper pill cups and unit-dose packaging. Factors influencing aerosolized particle generation included the method and intensity of crushing, and the type of pill crusher and container used. An optical particle counter was used to record particle counts in the breathing zone. The highest number of particles was produced when tablets in unit dose packaging were crushed with the MAXCRUSH pill crusher. An aggressive and vigorous procedure significantly increased the number of aerosolized particles generated across devices (p < 0.001) except MAXCRUSH with paper pill cups (p = 0.14). Most of the aerosolized particulate matter was produced when the crushed tablet was poured from its container into a cup of water. To minimize exposure, recommended control measures include substituting tablet medications with liquid forms, having pills crushed by the pharmacy, using a pill crushing syringe, limiting vigorous pouring of crushed medications from pill containers, and wearing a fit-tested N95 respirator.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".