Rates of Kidney Transplantation in Nations With Presumed Consent
Bibliographic record
Abstract
Letters7 June 2011Rates of Kidney Transplantation in Nations With Presumed ConsentLucy D. Horvat, MSc and Amit X. Garg, MD, PhDLucy D. Horvat, MScFrom University of Western Ontario, London, Ontario N6A 4G5, Canada.Search for more papers by this author and Amit X. Garg, MD, PhDFrom University of Western Ontario, London, Ontario N6A 4G5, Canada.Search for more papers by this authorAuthor, Article, and Disclosure Informationhttps://doi.org/10.7326/0003-4819-154-11-201106070-00014 SectionsAboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinkedInRedditEmail IN RESPONSE:We appreciate Dr. de Groot and colleagues' interest in our article. Multivariable analyses were conducted but then removed from an earlier version of our article to prioritize requested stratified analyses. Given the interest, the Table shows these multivariable analyses.Table. Association Between Rates of Kidney Transplantation and Presumed ConsentThe results are entirely consistent with our reported findings. Nations with presumed consent have higher rates of deceased donor kidney transplantation but lower rates of living donor kidney transplantation than nations with explicit consent. Any nation deciding to adopt presumed consent ...Reference1. Rithalia A, McDaid C, Suekarran S, Myers L, Sowden A. Impact of presumed consent for organ donation on donation rates: a systematic review. BMJ. 2009;:338. [PMID: 19147479] MedlineGoogle Scholar Author, Article, and Disclosure InformationAffiliations: From University of Western Ontario, London, Ontario N6A 4G5, Canada.Disclosures: Disclosures can be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=M10-1562. PreviousarticleNextarticle Advertisement FiguresReferencesRelatedDetailsSee AlsoInforming the Debate: Rates of Kidney Transplantation in Nations With Presumed Consent Lucy D. Horvat , Meaghan S. Cuerden , S. Joseph Kim , John J. Koval , Ann Young , and Amit X. Garg Rates of Kidney Transplantation in Nations With Presumed Consent Yorick J. de Groot , Hester F. Lingsma , and Erwin J.O. Kompanje Metrics 7 June 2011Volume 154, Issue 11Page: 778KeywordsConflicts of interestDisclosureKidneysLegislationRenal transplantationTransplantation ePublished: 7 June 2011 Issue Published: 7 June 2011 Copyright & PermissionsCopyright © 2011 by American College of Physicians. All Rights Reserved.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 | 0.002 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".