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GERM CELL TUMOURS
V. Kannan, B. K. Misra, Anila Kapadia, R. K. Bajpai, Supriya Deshpande, S. Almel, M. Sankhe, Kavita Desai, Muddassir Shaikh, Vivek Anand, A.M. Kannan, W.-Y. Teo, Jennifer L. Ross, Robert J. Bollo, Wan Tew Seow, Ah Moy Tan, Seok‐Gu Kang, Dong‐Sik Kim, X.-N. Li, Ching C. Lau, Carrie Mohila, Adekunle M. Adesina, Jack M. Su, Koichi Ichimura, Shintaro Fukushima, Yuko Matsushita, Arata Tomiyama, Tohru Niwa, Yuichi NAKAZATO, Toshihiro Kumabe, Motoo Nagane, Toshihiko Iuchi, M. Mizoguchi, Keiji Sugiyama, Mitsutoshi Nakada, Yonehiro Kanemura, Kiyotaka Yokogami, Tatsuhiro Shibata, Hirokazu Takami, Kohei Fukuoka, Tetsu Yanagisawa, Taishi Nakamura, Hideyuki Arita, Yoshitaka Narita, Soichiro Shibui, R. Nishikawa, Stephen A. Sands, W. Guerry, C. Kretschmar, B. Donahue, Jeffrey C. Allen, Ryutaro Nishikawa, Hiromi Nakamura, Yasuo Sawamura, Emi Hattori, Yoshiki Arakawa, Yasuhiro Kawabata, Tomokazu Aoki, S. Miyamoto, Naoki Kagawa, Ryuichi Hirayama, Yasunori Fujimoto, Yasuyoshi Chiba, M. Kinoshita, Koji Takano, Daisuke Eino, S. Fukuya, K. Nakanishi, F. Yamamoto, Y. Hashii, N. Hashimoto, Junichi Hara, T. Yoshimine, Matthew J. Murray, Jason Fangusaro, James C. Nicholson, D. Sumerauer, Michal Zápotocký, M. Churackova, Soňa Cyprová, Běla Malinová, Martin Kynčl, Michal Tichý, Joel M. Stary, Yasmin Lassen–Ramshad, G. von Oettingen, Mads Agerbæk, T. Ohnishi, Shohei Kohno, Azusa Inoue, Shintaro Iwata, Yoshiaki Kumon, S. Acharya, T.A. DeWees, Eric T. Shinohara, Sherry Perkins, Hiromi Kato, Hiroshi Fuji, Yoko Nakasu, Y. Ishida, S. Okawada, Qunying Yang, Chengcheng Guo, Zi‐Jiang Chen, Cécile Faure‐Conter, Cécile Verite, Anne Pagnier, Véronique Laithier, N. Entz-Werle, S. Gorde-Grosjean, Gilles Palenzuela, Philippe Lemoine, Hoang Anh Nguyen, Lan Bui, Ngoc, Manuela Cerbone, Ash Ederies, Laura Losa, C. Moreno, Kristi Sun, H. A. Spoudeas, Yoshiko Nakano, Koichi Okada, Yoshiyuki Kosaka, T. Nagashima, T. Soejima, Hiroyuki Sakamoto, J. Hara, Rolf‐Dieter Kortmann, M. L. Garre, Frank Saran, Gabriele Calaminus, Zulaiha Muda, Bina Menon, H. Ibrahim, E. J. A. Rahman, M. Muhamad, Intan Sharhida Othman, Asohan Thevarajah, Sylvia Cheng, John‐Paul Kilday, N. Laperriere, James M. Drake, E. Bouffet, Y. Matsusaka, Yusuke Watanabe, Ryoko Umaba, Jyunichi Hara, Claire Alapetite, A. Ruffier-Loubière, Ludovic De Marzi, S. Bolle, L. Claude, J.-L. Habrand, Hervé J. Brisse, Didier Frappaz, François Doz, F. Bourdeaut, R. Dendale, A. Mazal, N. Fournier‐Bidoz, M. Shirahata, J.-i. Adachi, Kazuhiko Mishima, K. Wakiya, Satoshi Yamashita, Mamoru Kato, Toshikazu Ushijima, Umberto Ricardi, Thomas Czech, James Hayden, Roshan Joseph, J. Hale, Holly Lindsay, M. Kogiso, Lin Qi, Teo Wan Yee, Yen‐Hua Huang, Hua Mao, Frank Y. Lin, P. Baxter, László Perlaky, Daniel R. Parsons, M. Chintagumpala, Xin Li, D. Osorio, David J. Vaughn, S. Gardner, Maciej Mrugala, M. Ferreira, C. Keene, Luis F. Gonzalez‐Cuyar, Adam O. Hebb, J. Rockhill, L. Wang, Shigeru Yamaguchi, M. Burstein, Ho‐Keung Ng, Zhiyan He, Atsushi Natsume, Shunsuke Terasaka, Thomas Dauser, Bill Whitehead, Jian Sun, D. Munzy, Richard A. Gibbs, Soraya Coelho Leal, David A. Wheeler, Girish Dhall, N. Robison, A. Judkins, Mark D. Krieger, Floyd H. Gilles, J. Park, Sung Uk Lee, T. Kim, Yue Kwan Choi, H. J. Park, Sang Hun Shin, J.-Y. Kim, N. Dhir, J. Khamani, A. Margol, Kenneth K. Wong, Barbara Britt, Anna Evans, Mary Jo Nelson, J. Grimm, Jonathan L. Finlay
Bibliographic record
VenueNeuro-Oncology · 2014
Typearticle
Languageen
FieldMedicine
TopicTesticular diseases and treatments
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsGerm cellBiologyGenetics
Abstract
No abstract in any covered source. Its absence is recorded, not treated as a negative.
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.
metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.020
Threshold uncertainty score0.065
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.020 | 0.007 |
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.
Opus teacher head0.014
GPT teacher head0.289
Teacher spread0.276 · how far apart the two teachers sit on this one work
Validation status
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.
The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther
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".
Quick stats
Citations26
Published2014
Admission routes1
Has abstractno