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Record W1528412525 · doi:10.1002/hep.26553

Introducing the AASLD President: J. Gregory Fitz

2013· article· en· W1528412525 on OpenAlexaboutno aff
Drew Feranchak

Bibliographic record

VenueHepatology · 2013
Typearticle
Languageen
FieldMedicine
TopicLiver Disease and Transplantation
Canadian institutionsnot available
FundersUniversity of California, San FranciscoAlpha Omega Alpha Honor Medical SocietyEpilepsy Foundation
KeywordsChapelWifePassionMedicineLibrary scienceManagementArt historyLawHistoryPolitical sciencePsychology

Abstract

fetched live from OpenAlex

A s an innovative researcher, dedicated teacher, astute clinician, and capable leader, J. Gregory Fitz, "Greg" (Fig. 1), has made significant contributions to the science and practice of hepatology and now continues to advance the mission of the AASLD as president of the organization.Greg was born in Lakeland, Florida, although shortly after his birth the family moved to Hickory, North Carolina.Greg's father was a cardiologist, the first in Hickory, and a prominent member of the community who soon became a member of the North Carolina Medical Board.Hickory is a small town located near the mountains of western North Carolina.Known for its handmade furniture and textile industry, its proximity to the Appalachian Mountains provides a myriad of outdoor opportunities; growing up in this beautiful area of the country, it is easy to understand Greg's lifelong passion for the outdoors.Shortly after arriving in Hickory, Greg was enrolled in the local kindergarten where he met his wife-to-be, Linda.In fact, he and Linda would go on to attend elementary school, high school, and even college together.Linda states that, as a child, "Greg was involved in everything"; an active member of the student body, president of the student council, wrestler, and high school football player.After high school he and Linda attended the University of North Carolina at Chapel Hill (UNC) where Greg majored in Chemistry and Linda in Special Education.Greg graduated from UNC summa cum laude as a Morehead scholar and, as a crowning achievement to his early successes, he and Linda were married.Greg's father was a significant influence in his decision to become a physician, as well as his decision to attend Duke University for medical school.The Fitz's had a strong history at Duke University, his father was also a Duke graduate and his mother previously worked for Dr. Eugene Stead, the Chair of Internal Medicine and a renowned medical educator, researcher, and founder of the Physician Assistant profession.Greg did not follow in his father's footsteps to become a cardiologist, however.In fact, Greg's early interest during medical school was in neurology and he worked in the laboratory of Dr. McNamara, performing research in experimental models of epilepsy.The young, aspiring researcher received the "Best Research Award" from the Epilepsy Foundation of America for this work.While it did not inspire a career as a neuroscientist, it nonetheless formed the foundation for his lifelong interest in ion channels and electrophysiology-the focus of his research activities for years to come.Greg graduated from Duke medical school AOA (Alpha Omega Alpha), gave the class graduation speech, promptly moved with Linda and their new daughter, Rebecca, from the east coast to the west coast, and entered the Internal Medicine residency program at the University of California at San Francisco (UCSF).Greg recalls that UCSF was an excellent training venue where residents worked independently and were given significant leadership opportunities early in their careers.Greg successfully completed his residency in Internal Medicine, spent a year as Chief Resident, and then became the Assistant Chief of Medicine.He was recognized at this early stage in his Fig. 1.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.062
Threshold uncertainty score0.206

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0620.021

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 head0.009
GPT teacher head0.233
Teacher spread0.224 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

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".

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Citations0
Published2013
Admission routes1
Has abstractyes

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