Review of economic data on closed system transfer drug for preparation and administration of hazardous drugs
Bibliographic record
Abstract
OBJECTIVES: The objectives of this study were to review economic data on the use of closed system drug transfer devices (CSTDs) for preparing and administering hazardous drugs, and to evaluate the quality of data reporting as defined by the Consolidated Health Economic Evaluation Reporting Standards (CHEERS). METHODS: All references from a recent Cochrane review about CSTDs were evaluated for inclusion. A literature review was also conducted. Articles containing economic data about the use of CSTDs were retained for analysis. Two researchers independently graded the articles according to the 24-item CHEERS checklist. RESULTS: Of the 138 articles identified initially, 12 were retained for analysis. Nine of these studies did not report acquisition costs or did not detail acquisition costs. Six studies reported economic benefits associated with the used of CSTDs, all related to extending the beyond-use date. The mean number of CHEERS criteria fulfilled by the included articles was 9.2 (SD 2.4). CONCLUSIONS: CSTDs are costly to acquire. However, few studies have examined the economic impact of these devices, and the existing studies are incomplete. As a result, hospitals planning to implement these devices will be unable to make a sound economic evaluation. Robust economic evaluation of CSTDs is needed.
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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 | 0.014 | 0.071 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.014 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".