Critical developments of 2017: a review of the literature from selected topics in transfusion. A committee report from the AABB Clinical Transfusion Medicine Committee
Bibliographic record
Abstract
BACKGROUND: The AABB compiles an annual synopsis of the published literature covering important developments in the field of transfusion medicine. An abridged version of this work is being made available in TRANSFUSION, with the full-length report available as Appendix S1 (available as supporting information in the online version of this paper). STUDY DESIGN AND METHODS: Papers published in late 2017 and 2018 are included, as well as earlier papers cited for background. Although this synopsis is comprehensive, it is not exhaustive, and some papers may have been excluded or missed. RESULTS: The following topics are covered: "big data" and "omics" studies, emerging infections and testing, platelet transfusion and pathogen reduction, transfusion therapy and coagulation, transfusion approach to hemorrhagic shock and mass casualties, therapeutic apheresis, and chimeric antigen receptor T-cell therapy. CONCLUSION: This synopsis may be a useful educational tool.
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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; 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".