Utilization of cross‐matched or <scp>HLA</scp>‐matched platelets for patients refractory to platelet transfusion
Bibliographic record
Abstract
BACKGROUND: Use of cross matching or HLA matching for donor selection is the basis of managing patients refractory to platelet (PLT) transfusion. Because of changes in patient care, we evaluated the effect of cross matching and HLA matching in patients refractory to PLT transfusion. STUDY DESIGN AND METHODS: We identified all patients who received either HLA-matched or cross-matched PLTs during a 3-year period at our medical center. Patient records were reviewed and laboratory data were collected. One- to 4-hour corrected count increments (CCIs) were calculated for transfusions given up to 72 hours before receiving these specialized units and the HLA-matched or cross-matched units themselves. RESULTS: Thirty-two patients were identified who received a total of 354 PLT transfusions. Of these, 161 were from unselected apheresis, 152 were cross matched, and 41 were HLA selected. The median CCI for random-donor transfusions was 0 (range, 0 × 10(9)-10.5 × 10(9)/L), for cross-matched PLT transfusions 1.7 × 10(9)/L (0 × 10(9)-5.1 × 10(9)/L), and for HLA-matched transfusions 1.2 × 10(9)/L (0 × 10(9)-13.9 × 10(9)/L). Only 25 and 30% of cross-match-compatible or HLA-selected units, respectively, gave 1- to 4-hour CCIs of more than 5.0 × 10(9)/L compared to 12% of the transfusions from random donors. There were no significant differences in the 1- to 4-hour CCIs when comparing random units with HLA-selected or cross-match-compatible units. There was also no significant difference when comparing the HLA-matched and cross-match-compatible PLT units with each other. CONCLUSIONS: The use of cross-match-compatible or HLA-matched units did not provide better increments in PLT count when compared to random nonselected units. Clinical factors may overpower immunologic matching.
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How this classification was reachedexpand
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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".