Time and Cost Savings with Bio-Set® Device in Reconstituting FVIII Concentrate.
Bibliographic record
Abstract
Abstract Introduction: Bio-Set® (Biodome, Issoire France) is a new needleless device developed for the reconstitution of a factor VIII concentrate, Kogenate® FS (Bayer HealthCare, Elkhart IN). Objectives: Quantitate time required to prepare FVIII concentrate for infusion and estimate the cost of medical waste produced using 3 reconstitution methods. Methods: 161 subjects (35 patients; 67 caregivers; and 59 nurses) were recruited from the US and Canada following an IRB-approved protocol. Reconstitution methods were Bio-Set®, the conventional 2 vial transfer needle reconstitution method, and 2 vial Baxject method (Baxter Healthcare, Westlake Village CA). Video and interviewer demonstrations were conducted, then participants practiced each reconstitution method once before performing a timed round. Diluent volume for the conventional reconstitution method and Baxject were controlled at 5 mL. After each timed round, participants separated reconstitution refuse into either medical waste or regular trash. The weights of component pieces were added and a cost for disposal of the medical waste was determined. Results: Participants completed preparation of the infusion with Bio-Set® in the shortest amount of time compared to the conventional method and Baxject (both p<0.0001). Results were similar across the 3 participant groups. The average weight of medical waste was lowest for Bio-Set® and highest for Baxject. The resulting disposal cost was significantly lower for Bio-Set® (p<0.0001). Conclusions: The results of the time study showed a reduction of 33% in infusion preparation time with the Bio-Set® when compared to the conventional method and 29% when compared to the Baxject. The cost of disposal of medical waste should be reduced with the use of the Bio-Set®.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.005 | 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".