Age of blood in inventory at a large tertiary care hospital
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: This major tertiary care centre transfers over 1000 red cell units monthly. Units are rotated as part of inventory management to ensure minimum wastage. In 1998, the expiration date of AS3 red cells was extended from 35 to 42 days, potentially affecting inventory. MATERIALS AND METHODS: The average age of the red cell units in inventory on any given day was evaluated to determine whether the extended expiration date would affect blood availability and to determine the feasibility of using blood at different ages for various purposes. Over a 6-month duration, 20 days were selected for review: units were categorized according to ABO group and Rh type and then analysed for age within certain categories. RESULTS: The average age of the blood in inventory was 1 to 2 weeks. The probability of having units less than 1-week old was highest for Group O and zero for Group B Rh(+) and Group AB Rh(+). More than 60% of the O Rh(-) blood was older than 28 days. CONCLUSION: The age of units in inventory varies with respect to ABO group, Rh type and weekday. In practice, the stock rarely reaches 42 days of age. Future studies on the effects of age of blood on patient outcome must consider the logistics of supply and the availability of blood of each group. Transfusion of large numbers of units of the same age and of a specific blood group and type may not always be possible.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".