Classical Clinical Trial Design in Testicular Cancer: Time to Move On
Bibliographic record
Abstract
Italian]. Minerva Ginecol 42:515-518, 1990 6. Jacoby VL, Autry A, Jacobson G, et al: Nationwide use of laparoscopic hysterectomy compared with abdominal and vaginal approaches. Obstet Gynecol 114:1041-1048, 2009 7. Seamon LG, Fowler JM, Richardson DL, et al: A detailed analysis of the learning curve: Robotic hysterectomy and pelvic-aortic lymphadenectomy for endometrial cancer. Gynecol Oncol 114:162-167, 2009 8. Leitao MM Jr, Gardner GJ, Briscoe G, et al: Comparison of robotically assisted and standard laparoscopic procedures in patients with endometrial cancer. Presented at the Society of Gynecologic Oncologists 42nd Annual Meeting on Women’s Cancer 2011, Orlando, FL, March 6-9, 2011 (abstr 296) 9. Jonsdottir GM, Jorgensen S, Cohen SL, et al: Increasing minimally invasive hysterectomy: Effect on cost and complications. Obstet Gynecol 117:1142-1149, 2011 10. Wikipedia: IBM 5100. http://en.wikipedia.org/wiki/IBM_5100 11. Apple: iPad. http://www.apple.com/ipad/
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 | 0.116 | 0.156 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.034 | 0.006 |
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".