Net haemoglobin increase from reinfusion of refrigerated vs. frozen red blood cells after autologous blood transfusions
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES Two main blood storage procedures can be used for storing red blood cells: refrigeration and freezing. Nevertheless, the efficiency of these procedures measured as the increase in haemoglobin after reinfusion compared with baseline has never been examined. The main objective was to examine which storage procedure yielded the largest increase in circulating haemoglobin after reinfusion compared to baseline. MATERIALS AND METHODS Equal volumes of blood from 15 men were withdrawn and stored either frozen or refrigerated as packed red blood cells. Serial measures of circulating haemoglobin by carbon monoxide rebreathing provided an opportunity to monitor recovery from anaemia, as well as the net increase in circulating haemoglobin after transfusion. RESULTS The post-thaw yield of haemoglobin in the bags was 72% after refrigerated storage compared with only 52% after freezing. Nevertheless, frozen storage allowed haemoglobin to fully recover before reinfusion, while the haemoglobin was 10% lower in the refrigerated group compared with baseline. After reinfusion, the haemoglobin levels were 11·5% higher than the baseline values in the group reinfused with frozen blood, while for the refrigerated group, haemoglobin levels were only 5·2% higher than baseline. CONCLUSION The relatively larger recovery from anaemia in the frozen group during storage more than compensated for the larger loss of haemoglobin during freezing and resulted in a larger net gain in haemoglobin. Based on the average 23 g per week recovery of haemoglobin, extending refrigerated storage to 7-8 weeks may yield sufficient time for patients to fully replenish harvested haemoglobin from three bags of blood without reliance on frozen storage of RBC.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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 teacher head, 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".