Bibliographic record
Abstract
Cell concentrating and washing techniques are widely accepted and believed to be beneficial to cardiac surgery patients. During cell processing, platelets, proteins, and clotting factors are wasted as the plasma is washed away by saline. Beneficial and costly plasma constituents are sacrificed for the sake of removing potentially harmful drugs, debris, and naturally activated cells and chemical mediators. An interactive Microsoft Excel spreadsheet was designed to input patient and autotransfusion system (ATS) reservoir blood values, processed centrifugal bowl data, and hospital allogeneic blood product concentration and cost information. The spreadsheet calculates the number of wasted platelets, grams of protein, and milligrams of fibrinogen. The calculator further estimates the number of units and cost of allogeneic blood products needed to replace the wasted blood components. The simulation allows for variable levels of platelet activation and protein removal during centrifugal cell processing. Specific case scenarios may be simulated with the calculator. If a known volume of residual extracorporeal circuit blood with a known hematocrit, platelet count, and protein concentration is diverted to the ATS reservoir to be processed and washed after bypass, the number of units of fresh frozen plasma, platelet packs, and albumin concentrate needed to replace the wasted proteins and platelets may be calculated. When typical end-bypass patient and blood bank product values are input, the cost to replace the wasted blood components in 1550 mL of residual circuit blood with allogeneic blood products is about US $2097. There are risks and costs associated with replacing the platelets, proteins, and clotting factors wasted during cell washing compared with other techniques such as whole blood ultrafiltration.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.038 | 0.006 |
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".