2015 proceedings of the National Heart, Lung, and Blood Institute's State of the Science in Transfusion Medicine symposium
Bibliographic record
Abstract
On March 25 and 26, 2015, the National Heart, Lung, and Blood Institute sponsored a meeting on the State of the Science in Transfusion Medicine on the National Institutes of Health (NIH) campus in Bethesda, Maryland, which was attended by a diverse group of 330 registrants. The meeting's goal was to identify important research questions that could be answered in the next 5 to 10 years and which would have the potential to transform the clinical practice of transfusion medicine. These questions could be addressed by basic, translational, and/or clinical research studies and were focused on four areas: the three "classical" transfusion products (i.e., red blood cells, platelets, and plasma) and blood donor issues. Before the meeting, four working groups, one for each area, prepared five major questions for discussion along with a list of five to 10 additional questions for consideration. At the meeting itself, all of these questions, and others, were discussed in keynote lectures, small-group breakout sessions, and large-group sessions with open discourse involving all meeting attendees. In addition to the final lists of questions, provided herein, the meeting attendees identified multiple overarching, cross-cutting themes that addressed issues common to all four areas; the latter are also provided. It is anticipated that addressing these scientific priorities, with careful attention to the overarching themes, will inform funding priorities developed by the NIH and provide a solid research platform for transforming the future practice of transfusion medicine.
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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 | 0.010 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.031 | 0.011 |
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".