Microbiologic Quality‐Control Study for the Purpose of Extending the Use of Transfer Sets on the Automix 3+3 and Micromix Automated Total Nutrient Admixture Compounding Pumps
Bibliographic record
Abstract
BACKGROUND: A quality-control study was undertaken by the departments of pharmacy and microbiology at St. Paul's Hospital (Vancouver, British Columbia, Canada) to evaluate the microbiologic safety of total nutrition admixtures (TNA) compounded by automated compounding pumps when the use of disposable transfer sets was extended from 1 day to 2 days. This study also evaluates the potential annual cost savings of this extended use. METHODS: Transfer sets and unused part containers of ingredients were left to sit overnight on the automated compounders after daily TNA manufacturing before a TNA sample was compounded for culturing. These TNA samples were cultured using a biphasic system consisting of a tryptic soy broth component and an agar slide component. Positive results were subcultured and isolates were identified by standard methods. Forty samples were collected and evaluated. RESULTS: Four bags grew Bacillus species, and 1 bag grew coagulase-negative staphylococci. The potential annual cost savings of this extended use was estimated to be approximately 35,000 Canadian dollars. CONCLUSIONS: The extended use of the disposable transfer sets cannot be instituted at the present time and should be reexamined when the cause(s) of the positive results are identified and corrected.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".