Performance of 0.8 percent reagent red blood cell panels after extended on-board analyzer utilization
Bibliographic record
Abstract
Abstract Automated pre-transfusion testing provides significant improvements in efficiency and productivity along with a reduction of the potential for errors. With reflex test capability and bidirectional Laboratory Information System interfacing, enhanced levels of effectiveness can be achieved in delivery of test results. To improve efficiency and productivity in our laboratory related to antibody identification (AbID) on our automated testing analyzer, we conducted a study that would allow for extended on-board utilization of our AbID reagent red blood cells (RRBCs). Our current process requires loading the 0.8 percent AbID RRBC panel onto the analyzer at the time of antibody detection and then removing and returning it to refrigerated storage once the AbID test has been completed. Our study was conducted at two hospital sites with an initial pilot study to determine the feasibility of using the RRBC panel on board with evaporation caps over a 7-day timeframe upon initial use of the panel and at two different timeframes later in the panel shelf life. Once the initial pilot study was completed and the feasibility of use established, a secondary study was initiated to determine if stability of reactivity was maintained using a rotational approach of time on board the analyzer compared with time in standard refrigerated storage. A 12-hour rotation at hospital 1 over a 2-week period and a 24-hour rotation at hospital 2 over a 3-week period were evaluated. Anti-c and anti-Fy a were used at one site while the other site used anti-E and anti-K. Respective negative controls were tested at both sites. Results of the pilot study demonstrated that the reactivity of the antibodies tested over the 7-day timeframe was maintained along with antibody specificity. The secondary study demonstrated sustained reactivity strength when using the rotational approach but showed occasional yet inconsistent results with respect to specific RRBC deterioration, fibrin in patient’s plasma, or indeterminate occurrence. Based on the results of the study, a 7-day on-board utilization protocol was established for routine use. The new extended on-board protocol offers enhanced performance, efficiency, and safety for our transfusion medicine operations.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".