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Record W1996237457 · doi:10.1089/omi.2014.0029

Bernard Lerer: Recipient of the 2014 Inaugural Werner Kalow Responsible Innovation Prize in Global Omics and Personalized Medicine (Pacific Rim Association for Clinical Pharmacogenetics)

2014· article· en· W1996237457 on OpenAlexaff
Vural Özdemir, László Endrényi, Şükrü Aynacıoğlu, Nicola Luigi Bragazzi, Collet Dandara, Edward S. Dove, Lynnette R. Ferguson, Christy J. Geraci, Ernst Hafen, Belgin Kesim, Eugene Kolker, Edmund J.D. Lee, Adrián LLerena, Muradiye Nacak, Kazutaka Shimoda, Toshiyuki Someya, Sanjeeva Srivastava, Brian Tomlinson, Effy Vayena, Louise Warnich, Ümit Yaşar

Bibliographic record

VenueOMICS A Journal of Integrative Biology · 2014
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicPharmacogenetics and Drug Metabolism
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPersonalized medicinePharmacogeneticsPrecision medicineSociologyHistoryMedicineBioinformaticsBiologyGenetics

Abstract

fetched live from OpenAlex

This article announces the recipient of the 2014 inaugural Werner Kalow Responsible Innovation Prize in Global Omics and Personalized Medicine by the Pacific Rim Association for Clinical Pharmacogenetics (PRACP): Bernard Lerer, professor of psychiatry and director of the Biological Psychiatry Laboratory, Hadassah-Hebrew University Medical Center, Jerusalem, Israel. The Werner Kalow Responsible Innovation Prize is given to an exceptional interdisciplinary scholar who has made highly innovative and enduring contributions to global omics science and personalized medicine, with both vertical and horizontal (transdisciplinary) impacts. The prize is established in memory of a beloved colleague, mentor, and friend, the late Professor Werner Kalow, who cultivated the idea and practice of pharmacogenetics in modern therapeutics commencing in the 1950s. PRACP, the prize's sponsor, is one of the longest standing learned societies in the Asia-Pacific region, and was founded by Kalow and colleagues more than two decades ago in the then-emerging field of pharmacogenetics. In announcing this inaugural prize and its winner, we seek to highlight the works of prize winner, Professor Lerer. Additionally, we contextualize the significance of the prize by recalling the life and works of Professor Kalow and providing a brief socio-technical history of the rise of pharmacogenetics and personalized medicine as a veritable form of 21(st) century scientific practice. The article also fills a void in previous social science analyses of pharmacogenetics, by bringing to the fore the works of Kalow from 1995 to 2008, when he presciently noted the rise of yet another field of postgenomics inquiry--pharmacoepigenetics--that railed against genetic determinism and underscored the temporal and spatial plasticity of genetic components of drug response, with invention of the repeated drug administration (RDA) method that estimates the dynamic heritabilities of drug response. The prize goes a long way to cultivate transgenerational capacity and broader cognizance of the concept and practice of responsible innovation as an important criterion of 21(st) century omics science and personalized medicine. A new call is presently in place for the 2016 PRACP Werner Kalow prize. Nominations can be made in support of an exceptional individual interdisciplinary scholar, or alternatively, an entire research team, from any region in the world with a record of highly innovative contributions to global omics science and/or personalized medicine, in the spirit of responsible innovation. The application process is straightforward, requiring a signed, 1500-word nomination letter (by the applicant or sponsor) submitted not later than May 31, 2015.

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)0.007
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.712
Threshold uncertainty score0.681

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.111
GPT teacher head0.483
Teacher spread0.371 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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

Citations7
Published2014
Admission routes1
Has abstractyes

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