Potassium as a Surrogate Marker of Debris in Cell-Salvaged Blood
Bibliographic record
Abstract
UNLABELLED: Centrifuge-based cell salvage systems have decreased the use of homologous blood transfusions. Although the evidence is anecdotal, the risk associated with the use of salvaged erythrocytes seems related to cellular and chemical contaminants. We sought to determine if potassium can be a surrogate marker for cellular debris and to measure the residual heparin level. Four units of expired whole blood were heparinized and concentrated with a Sequestra 1000 (Medtronics), Parker, CO) cell salvage device. The potassium, free hemoglobin, leukocyte, and platelet counts were sampled after each 250-mL normal saline wash aliquot, to a total wash volume of 1500 mL, whereas the heparin samples were obtained at wash volumes 0 and 1000 mL. Potassium, leukocyte, and platelet concentrations at wash volumes 0 and 250 mL were significantly greater than at all other volumes (P < 0.001). After 500 mL of saline wash, the change in these values was not significant. The mean (+/- SD) heparin levels (units/mL) at wash volumes 0 and 1000 mL were 10.2 (+/-3.1) and 0.11 (+/-0.02), respectively (P < 0.007). The r(2) values for free hemoglobin, leukocytes, and platelets versus potassium were 0.006, 0.992, and 0.995, respectively. No convenient test has been validated as an indicator of salvaged erythrocyte cleanliness. This in vitro study suggests that residual potassium concentration seems to be a good indicator of quality after washing with a contemporary intraoperative salvage system. IMPLICATIONS: No convenient test has been validated as an indicator of salvaged erythrocyte cleanliness. This in vitro study suggests that residual potassium concentration seems to be a good indicator of quality after washing with a contemporary intraoperative salvage system.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
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".