Promoting Inclusion, Diversity, and Equity in Pain Science
Bibliographic record
Abstract
Tonya M Palermo,1 Karen Deborah Davis,2 Didier Bouhassira,3 Robert W Hurley,4 Joel D Katz,5 Francis J Keefe,6 Michael E Schatman,7,8 Dennis C Turk,1 David Yarnitsky9 1Department of Anesthesiology & Pain Medicine, University of Washington, Seattle, Washington, USA; 2Department of Surgery and Institute of Medical Science, Krembil Brain Institute, University Health Network; University of Toronto, Toronto, Ontario, Canada; 3Inserm U987, APHP, UVSQ, Paris-Saclay University, Ambroise Pare Hospital, Boulogne-Billancourt, France; 4Department of Anesthesiology, Neurobiology and Anatomy, Wake Forest University School of Medicine, Winston-Salem, North Carolina, USA; 5Department of Psychology, York University, Toronto, Ontario, Canada; 6Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, North Carolina, USA; 7Department of Anesthesiology, Perioperative Care, and Pain Medicine, NYU Grossman School of Medicine, New York, New York, USA; 8Department of Population Health − Division of Medical Ethics, NYU Grossman School of Medicine, New York, New York, USA; 9Department of Neurology, Rambam Medical Center, and Laboratory of Clinical Neurophysiology, Technion Faculty of Medicine, Haifa, IsraelThis article is being published concurrently in Journal of Pain, PAIN, European Journal of Pain, Pain Medicine, Canadian Journal of Pain, Journal of Pain Research, Clinical Journal of Pain, and PAIN Reports. The articles are identical except for minor stylistic and spelling differences in keeping with each journal’s style. Citation from any of the journals can be used when citing this article.Correspondence: Tonya M Palermo, PhD, Department of Anesthesiology & Pain Medicine, University of Washington, P.O. Box 5371, M/S BC-3, Seattle Children’s Research Institute, Seattle, WA 98145-5005, USA, E-mail address: tonya.palermo@seattlechildrens.org
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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 | 0.089 | 0.120 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.008 | 0.023 |
| Scholarly communication | 0.015 | 0.018 |
| Open science | 0.002 | 0.045 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".