Low‐dose prophylactic platelet transfusions in recipients of an autologous peripheral blood progenitor cell transplant and patients with acute leukemia: a randomized controlled trial with a sequential Bayesian design
Bibliographic record
Abstract
BACKGROUND: Prophylactic platelet (PLT) transfusions are standard treatment for patients receiving high-dose chemotherapy, but the optimal dose is not known. A randomized controlled trial was undertaken to examine the effectiveness of low-dose PLT transfusions and to determine the need for further studies. STUDY DESIGN AND METHODS: Patients (n = 111) with acute leukemia or undergoing autologous peripheral blood progenitor cell (PBPC) transplantation were randomly assigned to receive low-dose (3 PLT units) or standard-dose (5 PLT units) prophylactic PLT transfusions and were monitored daily for bleeding. Using a sequential Bayesian design, the difference in major bleeding events was determined. RESULTS: The percentage of patients with major bleeding events was 10.7 percent (95% credible region, 5.1%-21.2%) in the low-dose PLT group and 7.3 percent (95% credible region, 2.9%-17.2%) in the standard-dose PLT group. The two additional events in the low-dose group occurred when the PLT count exceeded 100 x 10(9) per L. There is an 89 percent probability that the absolute increase in major bleeds is less than 10 percent with low-dose PLT transfusions. The number of minor bleeding events was higher in the standard-dose group. Patients receiving low-dose PLT transfusions received 25 percent fewer PLT units. There was an 89 percent probability that low-dose transfusions reduced PLT utilization in patients with acute leukemia and a 60 percent probability in patients undergoing PBPC transplantation. CONCLUSION: Low-dose PLT transfusions appear to be safe and effective and reduce PLT utilization. They should be further evaluated in clinical trials designed to evaluate equivalency.
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".