A special supplement to celebrate the 50th anniversary of PAIN: advancing pain research and management for the next 50 years
Bibliographic record
Abstract
Throughout 2025, we celebrated the 50th anniversary of PAIN, the journal of the International Association for the Study of Pain. A series of writings reflected on our history1 and paid tribute to Patrick Wall,2 the first Editor-in-Chief (EIC) of PAIN, and the three other previous EICs, Ronald Dubner,4 Allan Basbaum,6 and Frank Keefe.3 As 2025 comes to a close, it is my great pleasure to bring you this special Supplement issue of PAIN that synthesizes and highlights exciting advancements of the past while keeping an eye to the future. We hope the articles collected herein will serve to encourage and stimulate thought to advance research toward greater understanding of pain and improved pain management. It is an exciting time for the pain field as new technologies and bold, innovative thinking hold great promise to advance the study and management of pain toward improved outcomes and equity. This excitement has been captured beautifully in our cover image by Dr. Kathleen Sluka, which depicts the stairway climbed by people living with pain who experience a myriad of hardships and suffering. The image of a journey toward hope and relief is a theme that encapsulates the writings in this special Supplement, which present ideas and goals for the next 50 years of pain science, technology, health care, and community support toward a brighter future for those in pain. The Supplement opens with interviews of Drs. David Julius and Ardem Patapoutian,5 the 2021 Nobel Prize laureates in Physiology or Medicine for their work on receptors for temperature and touch that has led to significant advancements in our understanding of nociception and pain conditions. Their reflections humanize the pursuit of science, providing insight into their journeys and inspiration for future science, work–life balance, and mentorship of the next generation. The subsequent 28 papers summarize past work and look to the future of the pain field across a wide range of topics, from experimental work to translational, clinical, and community studies. The contributing authors are researchers and clinicians from many backgrounds, geographies, and career stages, whose diversity of perspectives provides insight, ideas, and inspiration. While no survey of such a broad range of disciplines and approaches can be truly exhaustive, I encourage you to read and consider the many themes that emerged, including the use of multidisciplinary approaches and collaboration, biomarker development, artificial intelligence, new and personalized models, and ethical and equitable frameworks. A universal theme across all these articles is a passionate optimism for a future in which pain relief is effective and accessible to all. Conflict of interest statement The author has no conflicts of interest to declare.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.163 | 0.070 |
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".