Survival Patterns for the Top Four Cancers in Canada: The Effects of Age, Region and Period
Bibliographic record
Abstract
No AccessJournal of UrologyUrological survey1 Jan 2006Survival Patterns for the Top Four Cancers in Canada: The Effects of Age, Region and Period A.-M. Ugnat, L. Xie, R. Semenciw, C. Waters, and Y. Mao A.-M. UgnatA.-M. Ugnat More articles by this author , L. XieL. Xie More articles by this author , R. SemenciwR. Semenciw More articles by this author , C. WatersC. Waters More articles by this author , and Y. MaoY. Mao More articles by this author View All Author Informationhttps://doi.org/10.1016/S0022-5347(05)00209-0AboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail "Survival Patterns for the Top Four Cancers in Canada: The Effects of Age, Region and Period." The Journal of Urology, 175(1), pp. 153–154 References 1 : Geographic patterns of prostate cancer mortality and variations in access to medical care in the United States. Cancer Epidemiol Biomarkers Prev2005; 14: 590. Google Scholar Surveillance and Risk Assessment Division, Centre for Chronic Disease Prevention and Control, Population and Public Health Branch, Health Canada, Ottawa, Ontario, Canada© 2006 by American Urological AssociationFiguresReferencesRelatedDetails Volume 175Issue 1January 2006Page: 153-154 Advertisement Copyright & Permissions© 2006 by American Urological AssociationMetricsAuthor Information A.-M. Ugnat More articles by this author L. Xie More articles by this author R. Semenciw More articles by this author C. Waters More articles by this author Y. Mao 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".