Active Surveillance Is the Preferred Approach to Clinical Stage I Testicular Cancer
Bibliographic record
Abstract
Craig R. Nichols, Virginia Mason Medical Center, Seattle, WA Bruce Roth, Washington University School of Medicine, St Louis, MO Peter Albers, University Hospital Heinrich-Heine, University of Dusseldorf, Dusseldorf, Germany Lawrence H. Einhorn and Richard Foster, Melvin and Bren Simon Cancer Center, Indiana University School of Medicine, Indianapolis, IN Siamak Daneshmand, Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA Michael Jewett and Padraig Warde, Princess Margaret Hospital, University of Toronto, Toronto, Ontario, Canada Christopher J. Sweeney and Clair Beard, Dana-Farber Cancer Institute, Brigham and Women’s Hospital, Boston, MA Tom Powles, Bart’s Cancer Institute, St Bartholomew’s Hospital, Queen Mary University of London, London, United Kingdom Scott Tyldesley and Alan So, British Columbia Cancer Agency–Vancouver Cancer Centre, University of British Columbia, Vancouver, British Columbia, Canada Christopher Porter and Semra Olgac, Virginia Mason Medical Center, Seattle, WA Karim Fizazi, Institute Gustave Roussy, University of Paris Sud, Paris, France Brandon Hayes-Lattin, Knight Cancer Institute, Oregon Health and Science University, Portland, OR Peter Grimison, Royal Prince Alfred Hospital, Sydney Cancer Centre, University of Sydney, Sydney, New South Wales, Australia Guy Toner, Peter MacCallum Cancer Center, University of Melbourne, Melbourne, Victoria, Australia Richard Cathomas, Kantonsspital Graubuenden, Chur, Switzerland Carsten Bokemeyer, University Medical Centre Eppendorf, Hamburg University, Hamburg, Germany Christian Kollmannsberger, British Columbia Cancer Agency–Vancouver Cancer Centre, University of British Columbia, Vancouver, British Columbia, Canada
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.005 |
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".