The operational impact of introducing cold stored platelets
Bibliographic record
Abstract
BACKGROUND: Cold stored platelets (CSP) undergo physical changes that make them better at initiating a clot. While cold stored platelets are superior for reducing bleeding in actively bleeding patients, room temperature platelets (RTP) are better for increasing platelet count in patients requiring a prophylactic transfusion. However, whether the overhead required to maintain a dual platelet inventory of both RTP and CSP could be compensated by reduced platelet wastage resulting from the longer shelf life of CSP has not been determined. STUDY DESIGN AND METHODS: A simulation model of a regional blood supply was built, with focus on the operations of a case hospital. Two scenarios were considered: "No-CSP," in which the hospital issues only RTP, and "CSP," in which the hospital issues both RTP and CSP Within the CSP scenario, conditions were tested under which the hospital receives only RTP and converts some to cold stored platelets and a second strategy where the hospital receives CSP from the regional supplier in addition to converting RTP. RESULTS: A centralized supply of CSP is necessary since on-site conversion is limited by platelet age. Product shortages decrease with increased CSP inventory, but CSP wastage increases. It was also determined that, because relatively few RTP units can be converted on-site, RTP wastage is not significantly decreased with the introduction of CSP. CONCLUSION: Given the clinical benefits for treatment of trauma, CSP is a desirable addition to a blood formulary. However, it is unlikely that significant reductions in RTP wastage will occur because of the introduction of CSP.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".