Prevalence of Male and Female Sexual Dysfunction is High Following Surgery for Rectal Cancer
Bibliographic record
Abstract
No AccessJournal of UrologyAdult urology1 Apr 2006Prevalence of Male and Female Sexual Dysfunction is High Following Surgery for Rectal Cancer S.K. Hendren, B.I. O’Connor, M. Liu, T. Asano, Z. Cohen, C.J. Swallow, H.M. Macrae, R. Gryfe, and R.S. McLeod S.K. HendrenS.K. Hendren More articles by this author , B.I. O’ConnorB.I. O’Connor More articles by this author , M. LiuM. Liu More articles by this author , T. AsanoT. Asano More articles by this author , Z. CohenZ. Cohen More articles by this author , C.J. SwallowC.J. Swallow More articles by this author , H.M. MacraeH.M. Macrae More articles by this author , R. GryfeR. Gryfe More articles by this author , and R.S. McLeodR.S. McLeod More articles by this author View All Author Informationhttps://doi.org/10.1016/S0022-5347(05)00876-1AboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail "Prevalence of Male and Female Sexual Dysfunction is High Following Surgery for Rectal Cancer." The Journal of Urology, 175(4), p. 1451 Department of Surgery, Mount Sinai Hospital, Toronto, Ontario, Canada© 2006 by American Urological AssociationFiguresReferencesRelatedDetails Volume 175Issue 4April 2006Page: 1451 Advertisement Copyright & Permissions© 2006 by American Urological AssociationMetricsAuthor Information S.K. Hendren More articles by this author B.I. O’Connor More articles by this author M. Liu More articles by this author T. Asano More articles by this author Z. Cohen More articles by this author C.J. Swallow More articles by this author H.M. Macrae More articles by this author R. Gryfe More articles by this author R.S. McLeod 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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".