Allan Basbaum: third Editor-in-Chief of PAIN and finding joy in pain research
Bibliographic record
Abstract
ABSTRACT: Allan Basbaum entered the field of pain research as an undergraduate at McGill University shortly after Pat Wall and Ron Melzack proposed Gate Control Theory. He has not wandered far from the field since then. Although Allan believes that serendipity has played a significant role in his success, it is equally important that one's mind be prepared to recognize the import of an event or finding. Over the past 50+ years, Allan has established a research program that is characterized by sustained innovation, pursuit of important questions, perseverance, early adoption of new methodologies, a willingness to embrace unexpected findings, and an enthusiastic endorsement of collaboration. The result has been a highly productive research program that continues to consistently advance our understanding of pain processes and simultaneously provides a unique environment that trains young investigators in multidisciplinary research and the convergent testing of hypotheses with complementary methodologies. This Pain Essay that pays tribute to the third Editor-in-Chief of PAIN describes the evolution of Allan's research program to date including some of his most important contributions to our understanding of the processes of nociception in the spinal cord and brainstem, as well as more recent studies encompassing biomarkers for the affective component of the pain experience. It also describes how PAIN thrived during his tenure as Editor-in-Chief, while providing a window into his ebullient personality. For Allan, pain research continues to be a life-long vocation that provides as much fun and satisfaction as a hobby.
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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 | 0.006 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.012 | 0.007 |
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".