Factors affecting the frequency of red blood cell outdates: an approach to establish benchmarking targets
Bibliographic record
Abstract
BACKGROUND: Benchmarking is a useful tool to identify best practices and to compare an organization's performance with that of similar peers, allowing for continuous quality improvement. In this study, a provincial database of red blood cell (RBC) product inventory/disposition in hospitals was analyzed to identify factors that affected RBC outdates and to systematically establish optimal target levels for RBC outdates. STUDY DESIGN AND METHODS: RBC inventory/disposition data for a 21-month period from 156 hospitals were analyzed using logistic regression techniques to identify factors that affected RBC outdating (month of the year, distance from the blood supplier, monthly transfusion activity, hospital type, and provincial region). The results were used to categorize hospitals into groupings that accounted for the factors affecting wastage. Within each grouping, the lower quartile was selected as the optimal target threshold. RESULTS: Three factors were identified as significantly affecting RBC outdating: distance from the blood supplier, mean monthly transfusion activity, and month of the year. Accounting for these variables, three hospital groupings were identified and benchmarking targets were established for mean monthly RBC outdating: There were 73 hospitals in Group 1 and their target level was 0.4 percent, 59 hospitals in Group 2 with a target of 1.1 percent, and 24 hospitals in Group 3 with a target of 20.3 percent. CONCLUSION: A method is described for establishing evidence-based benchmarking targets for RBC outdating that allows for hospitals to be grouped with similar peers taking into account logistic factors that impact on product outdating.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".