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Record W2315799215 · doi:10.1097/tp.0b013e318279d813

President of The Transplantation Society, 2010–2012

2012· editorial· en· W2315799215 on OpenAlexaff
Jeremy R. Chapman

Bibliographic record

VenueTransplantation · 2012
Typeeditorial
Languageen
FieldMedicine
TopicOrgan and Tissue Transplantation Research
Canadian institutionsUniversité du Québec
Fundersnot available
KeywordsTransplantationWifeMedicineMedical schoolFamily medicineManagementGerontologyPolitical scienceLawMedical educationInternal medicine

Abstract

fetched live from OpenAlex

Professor Gerhard Opelz (Fig. 1) was born in September 1944 in Austria. He was schooled in Austria, and after a period of compulsory military service in the Austrian Army, he attended medical school at the University of Vienna from 1963 to 1970.FIGURE 1: Professor Gerhard Opelz, President, The Transplantation Society, 2010–2012.He found both an interest in immunology and his future wife in Stockholm, where he worked as a physician at the Stockholm Blood Center and was awarded a fellowship by The Swedish Institute in 1970. A Fulbright fellowship in the following year saw him travel to California, where he commenced as a postgraduate research immunologist in the Department of Surgery, University of California School of Medicine. Working with Paul Terasaki, he became an associate professor in 1975, and a very productive period of research in human leukocyte antigen (HLA) and transplantation followed. He was recruited back across the Atlantic in 1981 as the Chair of Transplantation Immunology and the Director of the Department of Transplantation Immunology in the Institute of Immunology, University of Heidelberg, where he has remained to this day. He is one of the best known and most highly published people in the fields of both transplantation and histocompatibility, partly through the Collaborative Transplant Study (CTS), which he initiated in 1982, with currently approximately 400 participating centers in 46 countries. The growth of the data held by the CTS has been evident to anyone in the field, as shown in Figure 2. This has been one of our most important global resources, documenting over the years the various significant influences on transplantation outcomes. Not all of the results from the CTS have been welcomed nor indeed understood—at least initially. In the early 1980s, we learned from the CTS about the blood transfusion effect in renal transplantation, and then in the late 1980s, we learned from the same source that it has now dissipated. The importance of HLA matching has been an important theme of the work, especially with the introduction of more accurate DNA typing techniques, sensitive crossmatching techniques, and the definition of donor-specific antibodies to HLA and to major histocompatibility complex I–related chain A. However, the CTS has also contributed substantially in other areas that require large databases to see through the interfering noise (e.g., posttransplantation lymphoma, the importance of blood pressure control, the development of malignancies in the immunosuppressed patient, and trials of immunosuppression, especially through a randomized controlled study of steroid-free therapy).FIGURE 2: Cumulative growth data of the Collaborative Transplant Study, 1981–2012.Professor Opelz has been honored by many organizations, including the Schiff Award, German Society for Transfusion and Immunohematology; the Clas Högman Lecture, Swedish Society for Blood Transfusion; the Jean Dausset Lecture at the International Jean Dausset Symposium; the Terasaki Lecture, British Society for Histocompatibility and Immunogenetics; the Gold Medal of the Catalan Transplantation Society; the Terasaki Award, American Society for Histocompatibility and Immunogenetics; and the Ceppellini Lecture of the European Federation of Immunogenetics. He has served on many editorial boards and as president and on the councils of many professional societies. We are proud to have him serve as the President of The Transplantation Society.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.494
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.019
GPT teacher head0.309
Teacher spread0.289 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEditorial

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".

Quick stats

Citations0
Published2012
Admission routes1
Has abstractyes

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