ASTRO's Advances in Radiation Oncology Outstanding Reviewers for 2022
Bibliographic record
Abstract
Without our editorial board members and peer reviewers, scientific publishing would not be possible. The diligence and hours invested by our peer reviewers ensures the scientific integrity and quality of our manuscripts published in the American Society for Radiation Oncology's Advances in Radiation Oncology. Our reviewers work to eliminate biases and flawed methodology when identified. The process provides constructive feedback to authors that advances scientific knowledge and facilitates the publishing of valid scientific findings. We wish to recognize our outstanding reviewers and extend to them our gratitude for their hard work during the year 2022. Abdulla Al-Rashdan, MD, Dalhousie University Ahmed Abugharib, MD, PhD, University of Toronto Anurag K. Singh, MD, Roswell Park Comprehensive Cancer Center Arya Amini, MD, City of Hope National Medical Center Amar U. Kishan, MD, UCLA Christian Chinedu Okoye, MD, Mercy Medical Center Dana Casey, MD, University of North Carolina Health Edward Christopher Dee, MD, Memorial Sloan Kettering Cancer Center Emily Daugherty, MD, University of Cincinnati John Breneman, MD, Cincinnati Children's Hospital Medical Center Kang-Hyun Ahn, PhD, University of Chicago Nergiz Dagoglu, MD, Istanbul University Oncology Institute Paul J. Chuba, MD, PhD, St. John Macomb Oakland Hospital Parvez Memet Shaikh, MD, West Virginia University Ramiz Abu-Hijlih, MD, King Hussein Cancer Center Samuel Tay Chao, MD, Vantage Radiology & Diagnostic Services Scott R. Silva, MD, PhD, University of Louisville Health – Brown Cancer Center Sean Sachdev, MD, Northwestern Medicine Tarun K. Podder, PhD, Case Western Reserve University William C. Chen, MD, Long Island Jewish Medical Center at Northwell Health Ying Cao, MD, University of Kansas Cancer Center Robert C. Miller, Sharad Goyal, and C. Jillian Tsai report income from the American Society for Radiation Oncology.
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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 | 0.046 | 0.168 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.008 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.023 | 0.006 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.070 | 0.109 |
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".