Potential benefits of an alternative haemoglobin deferral strategy evaluated in seven countries
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: On-site donor deferral for low haemoglobin (Hb) levels poses significant challenges for blood establishments globally, leading to material wastage and consumption of valuable staff and donor time. Traditionally, donors are deferred based on a single visit's Hb measurement, without considering previous Hb levels and measurement variability. This study aims to quantify, in different settings, the potential impact of an alternative deferral algorithm based on historical mean Hb levels. MATERIALS AND METHODS: We retrospectively reassessed donor eligibility in 20,430,816 donations and deferrals in Australia, Belgium, Finland, France, the Netherlands, South Africa and the United States using an algorithm that considers a repeat donor eligible as long as their historical mean Hb is above the deferral threshold and deviations from the mean are consistent with anticipated measurement variability. We quantified the potential impact of the alternative algorithm by calculating the change in donations and deferrals. RESULTS: Across countries, the alternative algorithm may reduce low Hb deferrals between 30% and 70%. Additionally, in every country, a small proportion of current donors (~1%) donate who exhibit consistent low Hb levels. Balancing new deferrals and donations, the estimated net increase in donations across countries ranges between 0.7% and 3.3%. CONCLUSION: The alternative deferral algorithm based on mean Hb levels is a first step towards a more comprehensive assessment of Hb levels to determine donor eligibility. Further research is needed to refine the algorithm, to determine its long-term impact, to improve the model with information related to iron stores and recovery and to address the impact on donor safety.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".