Urologic oncology: extraordinary opportunities for discovery: highlights from the 2nd annual winter meeting of the society of urologic oncology december 1-2, 2001, bethesda, MD.
Bibliographic record
Abstract
The 2nd Annual Winter Meeting of the Society of Urologic Oncology (SUO) was held in the Natcher Conference Center of the National Institutes of Health in Bethesda, Maryland on December 1–2, 2001. The SUO was created in 1984 to include members interested in the care of patients with malignant genitourinary disease. The SUO develops educational and research initiatives, one of which is this winter oncology meeting, which is jointly sponsored by the National Cancer Institute and the Society of Urologic Oncology. Physicians, scientists, fellows, and medical and urologic oncologists attended to listen to state-of-the-art lectures presented by experts from all over the United States and Canada. Furthermore, there was a poster session with 50 abstract presentations, many of which were presented by fellows in training, which was a forum for basic research. This meeting was designed to facilitate discussion of important issues among members of the urologic oncology community at the National Institutes of Health. The meeting was broadly organized into three sessions devoted to the major urologic malignancies: transitional cell carcinoma (TCC), kidney cancer, and prostate cancer.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 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".