Déjà‐vu all over again: using simulation to evaluate the impact of shorter shelf life for red blood cells at <scp>H</scp>éma‐<scp>Q</scp>uébec
Bibliographic record
Abstract
BACKGROUND: Since the 1970s red blood cells (RBCs) have had a rated shelf life of 42 days. Recently, studies have suggested poorer patient outcomes when older blood is transfused. However, shortening the shelf life of RBCs may increase costs and lead to greater instances of outdates and shortages. STUDY DESIGN AND METHODS: A simulation method to evaluate the impact of a shorter shelf life for RBCs on a regional blood network was developed. A network model of the production and distribution system in the province of Quebec was built and validated. RESULTS: The model suggests that a shelf life of 21 or 28 days will have modest impact on outdate and shortage rates. A shelf life of 14 days will create significant challenges for both blood suppliers and hospitals and will result in systemwide outdate rates of 6.64% and shortage rates of 2.75%. The impact of a shorter shelf life for RBCs will disproportionately affect smaller and midsize hospitals. CONCLUSION: A shelf life of 28 or 21 days is feasible without excessive increases to systemwide outdate, shortage, or emergency ordering rates. Large hospitals will see minimal impact; smaller hospitals will see larger increases and may be unable to find inventory policies that maintain both low outdate and shortage rates. Reducing the shelf life to 14 days, or lower, results in significant challenges for suppliers and hospitals of all sizes. All hospitals will see an impact on outdate and shortage rates; overall systemwide outdate rates (6% or more) will reach levels that would currently be considered unacceptably high.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".