RBC deformability and clinical relevance in transfusion recipients
Bibliographic record
Abstract
Red blood cell (RBC) deformability-the ability of RBCs to change shape-is crucial for their passage through microvessels and effective oxygen delivery. Deformability depends on the cell's surface area-to-volume ratio, internal viscosity, and membrane elasticity. Deformability is assessed using single-cell methods like micropipette aspiration and bulk flow techniques such as ektacytometry. RBC deformability decreases during cold storage due to biochemical and structural changes known as storage lesions. Additional processes like cryopreservation and irradiation further impair the deformability of stored RBCs. Donor factors, including age, sex, lifestyle, and health, also influence RBC mechanical properties, affecting transfusion outcomes. Clinically, transfusing RBCs with reduced deformability is linked to impaired microvascular flow, decreased oxygen delivery, and faster clearance from the circulation, especially in patients with chronic or inflammatory conditions. Despite its importance, deformability testing is not yet standard in transfusion practice. New technologies offer potential for routine deformability assessment which could improve transfusion outcomes by optimizing RBC quality and matching units to patient needs.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".