Efficacy of <scp>HLA</scp>‐matched platelet transfusions for patients with hypoproliferative thrombocytopenia: a systematic review
Bibliographic record
Abstract
BACKGROUND: HLA-matched platelets (PLTs) are widely used to transfuse patients but the effectiveness of HLA matching has not been well defined and the cost is approximately five times the cost of preparing the random-donor PLTs. The objective of this systematic review was to determine whether HLA-matched PLTs lead to a reduction in mortality; reduction in frequency or severity of hemorrhage; reduction in HLA alloimmunization, refractoriness, or PLT utilization; or improvement in PLT count increment in patients with hypoproliferative thrombocytopenia. STUDY DESIGN AND METHODS: We conducted a literature search of MEDLINE, Cochrane Controlled Register of Clinical Trials, EMBASE, and PubMed databases to April 2012. RESULTS: A total of 788 citations were reviewed and 30 reports were included in the analysis. Most studies did not include technologies currently in use for HLA typing or detection of HLA antibodies as 75% were conducted before the year 2000. None of the studies were adequately powered to detect an effect on mortality or hemorrhage. HLA-matched PLTs did not reduce alloimmunization and refractoriness rates beyond that offered by leukoreduction, and utilization was not consistently improved. HLA-matched PLTs led to better 1-hour posttransfusion count increments and percentage of PLT recovery in refractory patients; however, the effect at 24 hours was inconsistent. CONCLUSION: The correlation of the PLT increment with other clinical outcomes and the effect of leukoreduction on HLA-matched PLT transfusion could not be determined. Prospective studies utilizing current technology and examining clinical outcomes are necessary to demonstrate the effectiveness of HLA-matched PLT transfusion.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".