Evaluation of noninvasive methods for the estimation of haemoglobin content in red blood cell concentrates
Bibliographic record
Abstract
OBJECTIVES: Red blood cell concentrates (RCCs) are the major blood component transfused to patients. There is a great variability in patient response, depending on both the patient's blood volume and haemoglobin content in the RCC. Standardisation of transfusion practice is needed to improve the prediction of patient outcome. AIM: We hypothesise that labelling of RCCs with haemoglobin content will add possibilities for the standardisation of transfusion practice. METHODS: Data from multiple international transfusion services regarding haemoglobin content and weight or volume of RCC were collected and analysed. RESULTS: We demonstrate a strong and highly significant correlation between haemoglobin content with both weight and volume of the RCCs. A linear regression model was used to assess these relationships, and it demonstrates how haemoglobin content can be estimated for different cell production processes. CONCLUSIONS: We recommend the use of weight or volume of the RCCs as the basis of estimating haemoglobin in the RCC and postulate that this can be used in future studies to explore the effects of a haemoglobin dose-based transfusion system. As the weight - and sometimes the volume - of the blood bag is easily accessible, in contrast to direct haemoglobin measurements from each individual unit, this method is feasible and simple.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".