Bibliographic record
Abstract
Translational research (TR) is a new categorization for the efforts of biomedical research and emphasizes efficiency in achieving population health improvements through the application of basic science knowledge in clinical practice. It will be argued that the current emphasis on speed and collaboration with industry established by national policies provides challenges to maintaining the integrity of scientific research. There is no agreed upon definition of TR and the current standard of judging the success of TR focuses on product production. I propose that the principles of beneficence and responsive justice should be used to inform the values of TR and to produce an agreed upon definition for TR. I will also suggest possible actions and changes to help incorporate the principles of beneficence and responsive justice into the translational framework that include greater reflection throughout the translational process about its priorities, increased role of implementation research, and the communication of negative results in addition to positive results.
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 | 0.392 | 0.441 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.010 | 0.064 |
| Scholarly communication | 0.021 | 0.018 |
| Open science | 0.007 | 0.013 |
| Research integrity | 0.054 | 0.061 |
| Insufficient payload (model declined to judge) | 0.007 | 0.006 |
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".