Translational research: an important integrated paradigm for transfusion medicine
Bibliographic record
Abstract
Translational research (TR) has many different potential definitions. There are two broad elements: one is research that serves as a bridge between basic science and the development/use of a therapeutic product/intervention; the second (also known as knowledge translation) explores why a clinically established product is not used more widely and/or further characterizes its optimal use. The first TR element has always been an integral part of transfusion medicine; it has previously been referred to as applied research or development. TR includes animal model research, and Phase I, II, and I/II clinical trials using human volunteers to determine safety and start evaluating efficacy of a novel therapy; some TR definitions also include definitive Phase III randomized controlled trials. TR usually needs a different infrastructure than basic research; funding may require public (government or university) and private (commercial companies) partnerships. Conducting TR requires a strong understanding of regulatory requirements and often necessitates the establishment of national or international multi‐center research programs supported by sophisticated information technology and a clinical coordinating center well versed in these disciplines. To this end, the National Heart, Lung, and Blood Institute in the U.S. not only offers its usual funding mechanisms but has also developed several other funding mechanisms to better support and coordinate such activities. There are multiple examples of successful TR in transfusion medicine; these include nucleic acid testing for infectious disease agents, the use of monoclonal antibodies in immunohematology, and the transfusion of pathogen inactivated platelet and plasma products in many countries. Scientific questions addressed by animal model research include red cell alloimmunization, graft versus host disease, transfusion related immunomodulation, and transfusion related acute lung injury. A new area of transfusion medicine TR is the production of functional mature RBCs from cultures of embryonic stem cells. The issue of how to best conduct TR still remains; challenges include finding mechanisms to adequately fund technological and product development, overcoming regulatory obstacles to the licensing of new technologies, and designing and funding large clinical trials to answer many of the remaining questions about transfusion triggers and best transfusion practices. The knowledge translation component of TR offers additional challenges; these require a different set of investigator skills and infrastructure in order to evaluate the effectiveness of interventions that can change clinical practice.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".