Bibliographic record
Abstract
BACKGROUND: Many countries maintain rare blood programs to provide access to blood for patients with complex serologies. These include a process to screen donors and a registry to record information about rare donors; blood agencies may also freeze some units. However, frozen blood is much more expensive than liquid blood. STUDY DESIGN AND METHODS: A two-phase approach to analysis was used to evaluate how rare a blood type must be before a frozen inventory is necessary and what screening rates are required to support a rare blood program. A simulation model was employed to evaluate the impact of inventory on patient access. RESULTS: Results suggested that, for 27 of 29 phenotypes managed by Canadian Blood Services, insufficient donors had been identified to ensure a stable inventory. Analytic results showed the screening rate necessary to ensure a stable inventory and the time frame to build a rare donor base. Twenty-nine simulation scenarios were executed to evaluate patient access to rare blood against inventory levels. Results show that some amount of frozen inventory is necessary for phenotypes rarer than 1 in 3000. However, holding more than two units apiece of O-, O+, A-, and A+ did not improve patient access. CONCLUSION: While some level of frozen blood is needed for rare blood, large inventories do not improve access. Modest amounts of frozen inventory, combined with increased door screening, provides the greatest chance of maximizing patient access.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".