Chemohormonal therapy in metastatic hormone-sensitive prostate cancer. Sweeney CJ, Chen YH, Carducci M, Liu G, Jarrard DF, Eisenberger M, Wong YN, Hahn N, Kohli M, Cooney MM, Dreicer R, Vogelzang NJ, Picus J, Shevrin D, Hussain M, Garcia JA, DiPaola RS. Department of Medicine; Department of Biostatistics and Computational Biology; Dana-Farber Cancer Institute, Boston; Harvard Medical School, Boston; Johns Hopkins University, Baltimore; University of Wisconsin Carbone Cancer Center; School of Medicine and Public Health; Madison; Fox Chase Cancer Center, Temple University Health System, Philadelphia; Indiana University Melvin and Bren Simon Cancer Center, Indianapolis; Mayo Clinic, Rochester, MN; University Hospitals Case Medical Center, Seidman Cancer Center; Cleveland Clinic Taussig Cancer Institute; Both in Cleveland; University of Virginia Cancer Center, Charlottesville; Comprehensive Cancer Centers of Nevada, Las Vegas; Siteman Cancer Center, Washington University School of Medicine, St. Louis; NorthShore University Health System, Evanston, IL; University of Michigan Comprehensive Cancer Center, Ann Arbor; Rutgers Cancer Institute of New Jersey, New Brunswick.N Engl J Med. 2015 Aug 20;373(8):737-46. [Epub 2015 Aug 5]. doi: 10.1056/NEJMoa1503747.
Bibliographic record
Abstract
No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.002 |
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".