Surface Contamination by Antineoplastics in Hospitals: An Observational Study for Mapping of Potential Contamination Associated with Handling Excreta of Babies through Diaper Management
Bibliographic record
Abstract
Abstract Background In the hospital setting, trace contamination with hazardous medications comes primarily from the manipulation of containers used in preparing and administering drugs. However, some traces of medications also come from the excreta of patients. Methods This descriptive exploratory study involved direct observation and discussion. The aim was to map potential contamination associated with handling babies’ excreta through diaper management. The study was conducted at CHU Sainte Justine (Montréal, Québec, Canada), a 500-bed mother and child facility with 38 beds for hematology-oncology and bone marrow transplant. A list of key steps related to the management of diapers by a parent or caregiver on a pediatric unit was established by the investigators. A data collection grid was then developed and reviewed by a member of the research team. Results A total of six diaper changes, by six distinct individuals, were observed in August and September 2019. Transport of a soiled diaper for weighing outside the baby’s room by an additional caregiver was also observed and recorded. In total, 25 individual steps in diaper management and 28 potential failure modes were identified through mapping. Conclusions Changing a baby’s diaper involves many individual steps, which are subject to numerous failure modes that can contribute to contamination with traces of hazardous drugs. A good understanding of these process steps and failure modes is desirable to better train caregivers and parents to reduce trace contamination with hazardous drugs.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".