Prevalence and Incidence of Viral Infections among Musculoskeletal Tissue Donors and First-Time Blood Donors
Bibliographic record
Abstract
Letters20 May 2008Prevalence and Incidence of Viral Infections among Musculoskeletal Tissue Donors and First-Time Blood DonorsFelix Yao, MBBS, Clive Seed, BSc, David Wood, MBBS, MS, and Ming-Hao Zheng, PhD, DMFelix Yao, MBBSFrom University of Western Australia, Perth WA 6009, Australia.Search for more papers by this author, Clive Seed, BScFrom University of Western Australia, Perth WA 6009, Australia.Search for more papers by this author, David Wood, MBBS, MSFrom University of Western Australia, Perth WA 6009, Australia.Search for more papers by this author, and Ming-Hao Zheng, PhD, DMFrom University of Western Australia, Perth WA 6009, Australia.Search for more papers by this authorAuthor, Article, and Disclosure Informationhttps://doi.org/10.7326/0003-4819-148-10-200805200-00017 SectionsAboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinkedInRedditEmail Background: Musculoskeletal tissue is second only to blood as the most frequently transplanted human tissue, and there continues to be an enormous demand for these allografts throughout the world. Little information is known about the risks associated with musculoskeletal tissue donation. Viral infection is a potential complication of tissue transplantation, and the prevalence and incidence of infection among tissue donors provides an indication of the relative safety of the tissue supply in different countries.Objective: To define the prevalence and incidence of markers of HIV, hepatitis B virus, hepatitis C virus, and human T-cell lymphotropic virus (HTLV) in musculoskeletal tissue ...References1. Zou S, Dodd RY, Stramer SL, Strong DM; Tissue Safety Study Group. Probability of viremia with HBV, HCV, HIV, and HTLV among tissue donors in the United States. N Engl J Med. 2004;351:751-9. [PMID: 15317888] CrossrefMedlineGoogle Scholar2. Zahariadis G, Plitt SS, O'Brien S, Yi QL, Fan W, Preiksaitis JK. Prevalence and estimated incidence of blood-borne viral pathogen infection in organ and tissue donors from northern Alberta. Am J Transplant. 2007;7:226-34. [PMID: 17109730] CrossrefMedlineGoogle Scholar3. Galea G, Dow BC. Comparison of prevalence rates of microbiological markers between bone/tissue donations and new blood donors in Scotland. Vox Sang. 2006;91:28-33. [PMID: 16756598] CrossrefMedlineGoogle Scholar Author, Article, and Disclosure InformationAffiliations: From University of Western Australia, Perth WA 6009, Australia.Acknowledgment: The authors thank David Morgan, Morris Benkovich, and Ron Simard from the Queensland Bone Bank; Kellie Hamilton and Vicky Winship from the Donor Tissue Bank of Victoria; and Anne Cowie and Joyleen Winter from the Perth Bone and Tissue Bank for helping with data procurement and analysis.Grant Support: By a grant from the Sir Charles Gairdner Hospital Research Foundation awarded to Drs. Zheng and Yao.Disclosures: None disclosed. PreviousarticleNextarticle Advertisement FiguresReferencesRelatedDetails Metrics 20 May 2008Volume 148, Issue 10Page: 792-794KeywordsBiomarkersBlood donorsBoneHIVHepatitis B virusHepatitis C virusPrevention, policy, and public healthTotal hip arthroplastyViral transmission and infection ePublished: 20 May 2008 Issue Published: 20 May 2008 CopyrightCopyright © 2008 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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 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".