Ronald Dubner: pioneer in pain research, founding member of the International Association for the Study of Pain, and former Editor-in-Chief of PAIN
Bibliographic record
Abstract
ABSTRACT: Ronald Dubner (1934-2023) was a "giant" in the field of pain. His more than 5 decades of research programs at the US National Institutes of Health and the University of Maryland resulted in important discoveries that considerably advanced our understanding of the neural and nonneural processes underlying acute and chronic pain and their behavioral and clinical correlates. Through his multidisciplinary and translational research approaches, his novel findings as well as his training as a dentist and neuroscientist, Ron was able to bring to the attention of the pain field the clinical implications of these findings and thereby positively influence the clinical management of pain. Also especially notable were his mentorship of numerous pain scientists and clinicians, many of whom went on to develop their own research programs that significantly benefitted the pain field. Ron also played leadership roles in the International Association for the Study of Pain and other scientific organizations, and his editorial positions for the PAIN journal significantly and positively influenced the journal's stature and its impact on the pain field. This article, which is part of the journal's series this year that is celebrating its 50th anniversary, highlights Ron's research and related activities during his years at the National Institutes of Health and University of Maryland and includes comments that Ron himself made about these activities. The article also considers his background and personal attributes that underpinned the many contributions that Ron Dubner made to the pain field, including those to the International Association for the Study of Pain and PAIN .
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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 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".