CORRELATION OF HISTOPATHOLOGICAL FEATURES TO TUMOR SIZE AMONG PATIENTS WITH T1A RENAL TUMORS
Bibliographic record
Abstract
You have accessJournal of Urology1 Apr 2009CORRELATION OF HISTOPATHOLOGICAL FEATURES TO TUMOR SIZE AMONG PATIENTS WITH T1A RENAL TUMORS Darwin Lim, Mazen Abdelhady, Paul Martin, Carlos Martinez, Venu Chalasani, Stephen Pautler, Jonathan I Izawa, and Joseph L Chin Darwin LimDarwin Lim More articles by this author , Mazen AbdelhadyMazen Abdelhady More articles by this author , Paul MartinPaul Martin More articles by this author , Carlos MartinezCarlos Martinez More articles by this author , Venu ChalasaniVenu Chalasani More articles by this author , Stephen PautlerStephen Pautler More articles by this author , Jonathan I IzawaJonathan I Izawa More articles by this author , and Joseph L ChinJoseph L Chin More articles by this author View All Author Informationhttps://doi.org/10.1016/S0022-5347(09)61009-0AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail "CORRELATION OF HISTOPATHOLOGICAL FEATURES TO TUMOR SIZE AMONG PATIENTS WITH T1A RENAL TUMORS." The Journal of Urology, 181(4S), p. 356 © 2009 by American Urological AssociationFiguresReferencesRelatedDetails Volume 181Issue 4SApril 2009Page: 356 Advertisement Copyright & Permissions© 2009 by American Urological AssociationMetricsAuthor Information Darwin Lim More articles by this author Mazen Abdelhady More articles by this author Paul Martin More articles by this author Carlos Martinez More articles by this author Venu Chalasani More articles by this author Stephen Pautler More articles by this author Jonathan I Izawa More articles by this author Joseph L Chin More articles by this author Expand All Advertisement PDF downloadLoading ...
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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 | 0.000 | 0.003 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".