A comparative study of common techniques used to measure haemolysis in stored red cell concentrates
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: There is no standardized method of measuring the parameters for haemolysis determination of red cell concentrate (RCC). Three haemoglobin quantification methods (automated analyser, Harboe and Drabkin's) and two methods of haematocrit measurement (automated analyser and microcapillary centrifugation) were evaluated for use with RCC. MATERIALS AND METHODS: Twenty stored RCC were assayed for total haemoglobin, supernatant haemoglobin and haematocrit. RESULTS: Drabkin's and Harboe methods were linear (r(2) > or = 0.995) over 0.015-220 g/l haemoglobin. Overestimation by Drabkin's increased from 0% at 220 g/l to 137% at 0.015 g/l haemoglobin. Harboe values generally stayed within 6% of expected while haematology analyser values had a maximum 11% underestimation above 10 g/l. Analyser total haemoglobin was significantly lower (202 +/- 22 g/l) than Drabkin's (224 +/- 24 g/l) and Harboe (222 +/- 22 g/l) values. Haematocrit was greater via the analyser (65.7 +/- 5.7%) than with microcapillary centrifugation (59.3 +/- 5.7%). CONCLUSIONS: Harboe and Drabkin's methods are suitable for measuring total haemoglobin and supernatant haemoglobin in RCC. The analyser gave higher haematocrit values (11% on average) than did microcapillary centrifugation.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".