A comparative study of common techniques used to measure haemolysis in stored red cell concentrates
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 |
| 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) | 0.000 | 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 it