Bibliographic record
Abstract
Translational research is a new and important way of thinking about research. It is a major priority of the National Institutes of Health (NIH) in the United States. NIH has created the Clinical and Translational Science Awards to promote this priority. NIH has defined T1 and T2 phases of translational research in the medical field, in order to bring the benefits of scientific results into communities. Current discussions focus on clarifying the subsequent phases of translational research necessary to achieve the intended social impact of research. This article suggests that T3 translational research could aim at getting research out of the highly controlled environment of the academic health center and into the real world. Likewise, it suggests T4 translational research could aim at policy development through policy analysis and evaluation, cost-benefit analysis, and surveillance studies. Translational research has challenges beyond definitions. Translational research is incomplete at any level unless appropriate steps are taken to communicate the results to relevant stakeholders. It appears that communication is currently suboptimal at all levels of translation. Translational research also faced challenges in research funding and training of researchers. Translational thinking should be a key part of research policy and research practice at all levels.
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 | 0.482 | 0.420 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.011 | 0.074 |
| Scholarly communication | 0.033 | 0.037 |
| Open science | 0.011 | 0.028 |
| Research integrity | 0.028 | 0.038 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".