Bibliographic record
Abstract
PURPOSE OF REVIEW: To review data about transfusion-transmitted infections so as to assess potential safety benefits of applying pathogen inactivation technology to platelets. RECENT FINDINGS: Residual bacterial risk still exists. Multiple arbovirus epidemics continue to occur and challenge blood safety policy makers in nonendemic developed countries. There is new documentation of transfusion transmission of dengue and Ross River viruses, and new or increased concern about chikungunya and Zika viruses. Pathogen inactivation has been shown to inactivate almost all bacterial species and several epidemic arboviruses that pose a transfusion transmission risk. The two available platelet pathogen inactivation technologies show different levels of pathogen inactivation as measured by in-vitro infectivity assays; the clinical significance of this finding is not known. SUMMARY: Pathogen inactivation can mitigate infectious risk and should do so more completely than other interventions such as donor questioning, donor/component recall, or donor testing. However, pathogen inactivation increases the cost of the pathogen-reduced blood component, which is a significant obstacle in the current healthcare environment. This may inhibit the ability to move forward with an effective new paradigm for blood safety that fulfills the implicit public trust in the blood system.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".