A pilot study of the possibility and the feasibility of haemoglobin dosing with red blood cells transfusion
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Red blood cell concentrates (RBCs) are the major blood component transfused. Although the haemoglobin content is variable, the transfusion dose is prescribed as units of red cell concentrates. Thus, by chance, large volume patients may receive a low haemoglobin dose and low volume patients may be transfused with haemoglobin-rich RBCs. The aim of this study was to evaluate whether the haemoglobin increment (grams per litre) in the patient can be predicted from the haemoglobin dose (in grams) transfused, with and without correction for estimated blood volume. If this is true, it may be possible to achieve the predicted transfusion outcome by selecting RBCs for each patient. MATERIALS AND METHODS: Haemodynamically stable patients scheduled for day treatment with transfusion of RBCs were recorded. A total of 52 transfusions episodes, 27 for women and 25 for men, were recorded. Blood volumes were estimated, haemoglobin content in the RBCs was measured before transfusion, and pre- and post-transfusion haemoglobin concentrations were obtained. RESULTS: The haemoglobin content of the RBCs prepared for transfusion showed a wide range, varying from 38.7 g/unit to 69.0 g/unit. There were statistically significant correlations between haemoglobin concentration in the RBCs and haemoglobin increment in patients. CONCLUSION: Post-transfusion increment in circulating haemoglobin can be predicted from the haemoglobin content of transfused cells, but knowledge of the patient's blood volume improves the accuracy of prediction. It may be feasible to select the high haemoglobin content RBC for patients with largest blood volume and vice versa.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".