Bibliographic record
Abstract
Evidence-based clinical reporting: a need for improvement N umerous clinical studies, using a variety of experimental 1 research designs, have investigated the clinical importance of WBC reduction to prevent transfusion-associated adverse effects such as HLA alloimmunization, febrile nonhemolytic transfusion reactions (FNHTR), transplant rejection, immunomodulation, GVHD, and transmission of HTLV-I, CMV, and other infectious agents.[1][2][3][4][5] These studies provide good evidence and a general consensus exists that WBC reduction will reduce the frequency of FNHTR, HLA alloimmunization, and CMV infection; however, the other indications remain controversial despite numerous attempts to define whether a benefit exists.To add to the controversy, some countries have implemented, or will be implementing, universal WBC reduction of RBC and platelet components.6 These countries include: Canada, Germany, New Zealand, Luxembourg, the United Kingdom, Ireland, Portugal, the Netherlands, and France.The rationale to support this policy decision is based largely on the evidence of beneficial immunologic effects and perceived cost benefits.However, in the United States the decision about whether to universally WBC reduce the blood supply has failed to reach a consensus and has sparked a passionate and controversial debate.4,7,8 Why does this controversy continue to confuse the decision-making process in the United States, particularly when there are a multitude of published clinical studies evaluating the potential benefits of WBC reduction?There are two possible reasons.First, the evidence for some indications remains controversial because of conflicting study results and methodologic concerns related to study design.Second, there have been no peer-reviewed published studies that demonstrate the clinical benefit of universal WBC reduction compared to selective WBC reduction for specific patient populations considered to be at high risk.This issue of TRANSFUSION contains three additional studies that address the potential various benefits of WBC reduction.Volkova and colleagues 9 report a case control study examining the impact of WBC reduction on the cost of hospital care for patients undergoing coronary artery bypass graft surgery.The issue of whether WBC reduction decreases the rate of postoperative infections in patients undergoing cardiac surgery has been addressed by Wallis et al., 10 using a randomized controlled study design.In addition, Dzik and colleagues 11 report
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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 | 0.700 | 0.902 |
| Meta-epidemiology (narrow) | 0.005 | 0.009 |
| Meta-epidemiology (broad) | 0.023 | 0.012 |
| Bibliometrics | 0.029 | 0.041 |
| Science and technology studies | 0.006 | 0.031 |
| Scholarly communication | 0.054 | 0.051 |
| Open science | 0.026 | 0.018 |
| Research integrity | 0.032 | 0.048 |
| Insufficient payload (model declined to judge) | 0.019 | 0.013 |
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".