Bibliographic record
Abstract
his is the beginning of the third publication year of JDR Clinical & Translational Research and, on this anniversary, it is valuable to step back and revisit the concepts on which we based the journal.In 2015, the International Association for Dental Research and American Association for Dental Research boards made the decision to develop a journal for clinical research in oral health that also emphasizes the importance of research translation.Thus, they titled this journal JDR Clinical & Translational Research.Over the past 2 years, we have been publishing original clinical research reports and have grouped these by their methodologies.At this point, we will further classify our published reports by indicating where they sit on the Biomedical Research Translation Continuum.This should aid authors and readers in understanding how the findings from each study can be moved forward to most efficiently result in the improvement of health and well-being for the population.Clinical and translational research (CTR) involves that part of the Biomedical Research Translation JCTXXX10.1177/2380084417743430JDRClinical & Translational ResearchLooking Backward and Forward research-article2017
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 | 0.016 | 0.075 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.017 | 0.010 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.025 | 0.033 |
| Insufficient payload (model declined to judge) | 0.032 | 0.028 |
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".