The epidemiology of platelet transfusions: an analysis of platelet use at 12 US hospitals
Bibliographic record
Abstract
BACKGROUND: Using the Recipient and Donor Epidemiology Study-III (REDS-III) recipient and donor databases, we performed a retrospective analysis of platelet use in 12 US hospitals that were participants in REDS-III. STUDY DESIGN AND METHODS: Data were electronically extracted from participating transfusion service and blood center computer systems and from medical records of the 12 REDS-III hospitals. All platelet transfusions from 2013 to 2016 given to patients aged 18 years and older were included in the analysis. RESULTS: There were 28,843 inpatients and 2987 outpatients who were transfused with 163,719 platelet products (103,371 apheresis, 60,348 whole blood derived); 93.5% of platelets were leukoreduced and 72.5% were irradiated. Forty-six percent were transfused to patients with an International Classification of Diseases, 9th/10th Revision (ICD-9/10) diagnosis of leukemia, myelodysplastic syndrome (MDS), or lymphoma. The general ward and the intensive care unit (ICU) were the most common issue locations. Only 54% of platelet transfusions were ABO identical; and 60.6% of platelet transfusions given to Rh-negative patients were Rh positive. The most common pretransfusion platelet count range for inpatients was 20,000 to 50,000/μL, for outpatients it was 10,000 to 20,000/μL. Among ICU patients, 35% of platelet transfusion episodes had a platelet count of greater than 50,000/μL; this was only 8% for general ward and 2% for outpatients. The median posttransfusion increment, not corrected for platelet dose and/or patient size, ranged from 12,000 to 20,000/μL for inpatients, and from 17,000 to 27,000/μL for outpatients. CONCLUSIONS: These data from one of the largest reviews of platelet transfusion practice to date provide guidance for where to focus future clinical research studies and platelet blood management programs.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".