Clinical trials evaluating pathogen‐reduced platelet products: methodologic issues and recommendations
Bibliographic record
Abstract
BACKGROUND: Several randomized trials of platelet (PLT) products have been conducted with different study designs, endpoints, and analyses. The purpose of this article is to discuss methodologic issues in the design and analysis of PLT transfusion trials evaluating pathogen reduction technology and make recommendations for the conduct of future trials. STUDY DESIGN AND METHODS: Six randomized clinical trials of pathogen-inactivated PLT products are reviewed and associated methodologic issues are discussed. RESULTS: The variation in the trial designs, outcomes, and methods of analysis suggest the need to harmonize the way trials of pathogen-reduced PLT products are conducted to facilitate comparisons between studies and the synthesis of results. Recommendations are made with this goal in mind and to increase the rigor and relevance of findings from future trials. CONCLUSIONS: Future randomized trials of pathogen-reduced PLT products should be based on a clearly stated hypothesis driven by an important research question, a design that is optimal for the research question, outcomes that relate to the research question, clearly defined observation periods, and statistical analyses that lead to valid tests of these hypotheses and associated estimates of treatment effect.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.601 | 0.826 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.011 | 0.005 |
| Research integrity | 0.019 | 0.019 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".