AABB Committee Report: reducing transfusion‐transmitted cytomegalovirus infections
Bibliographic record
Abstract
Transfusion-transmitted cytomegalovirus (TT-CMV) is often asymptomatic, but certain patient populations, such as very low birth weight neonates, fetuses requiring intrauterine transfusion, pregnant women, patients with primary immunodeficiencies, transplant recipients, and patients receiving chemotherapy or transplantation for malignant disease, may be at risk of life-threatening CMV infection. It is unclear whether leukoreduction of cellular blood components is sufficient to reduce TT-CMV or whether CMV serological testing adds additional benefit to leukoreduction. The AABB CMV Prevention Work Group commissioned a systematic review to address these issues and subsequently develop clinical practice guidelines. However, the data were of poor quality, and no studies of significant size have been performed for over a decade. Rather than creating guidelines of questionable utility, the Work Group (with approval of the AABB Board of Directors) voted to prepare this Committee Report. There is wide variation in practices of using leukoreduced components alone or combining CMV-serology and leukoreduction to prevent TT-CMV for at-risk patients. Other approaches may also be feasible to prevent TT-CMV, including plasma nucleic acid testing, pathogen inactivation, and patient blood management programs to reduce the frequency of inappropriate transfusions. It is unlikely that future large-scale clinical trials will be performed to determine whether leukoreduction, CMV-serology, or a combination of both is superior. Consequently, alternative strategies including pragmatic randomized controlled trials, registries, and collaborations for electronic data merging, nontraditional approaches to inform evidence, or development of a systematic approach to inform expert opinion may help to address the issue of CMV-safe blood components.
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 | 0.086 | 0.090 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.031 | 0.012 |
| Insufficient payload (model declined to judge) | 0.012 | 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; 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".