Closed-system transfer device use with oncology biologics: A survey of Canadian healthcare practitioners
Bibliographic record
Abstract
INTRODUCTION: Closed-system transfer devices (CSTDs) were introduced into clinical practice to protect healthcare practitioners (HCPs) from exposure to hazardous drugs. However, ambiguous guidelines have led to confusion as to when CSTD use is required, as institutes are instructed to maintain their own hazardous drug lists and determine the appropriate level of personal protective equipment for their staff. This study seeks to understand the current use of CSTDs by Canadian oncology HCPs, the influence of various stakeholders on their use and the challenges faced by HCPs surrounding the use of these medical devices. METHODS: The researchers compiled a set of questions to inform on the current use of CSTDs in clinical practice and administered an online survey to oncology HCPs across Canada. RESULTS: The results indicate that though CSTD use is common in Canadian oncology practice settings, there is variation in the extent of the use of these devices across provinces and with which products these devices are used. The survey results also show that the top challenges with the use of CSTDs include cost, lack of information on the compatibility of a CSTD with a drug product, and CSTD impact on drug quality. Many respondents are aligned that regulatory bodies are more likely to influence the use of CSTDs with specific drug products than drug manufacturers. CONCLUSION: Guidelines for the application of CSTDs in clinical practice vary and are often ambiguous. Regulatory bodies are uniquely positioned to provide healthcare institutions with more clarity on when CSTD use is appropriate.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".