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Record W2524871109 · doi:10.1097/gox.0000000000001042

Fu-Chan Wei—Surgeon, Innovator, and Leader of the Legendary Chang Gung Microsurgery Center

2016· article· en· W2524871109 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePlastic & Reconstructive Surgery Global Open · 2016
Typearticle
Languageen
FieldMedicine
TopicReconstructive Surgery and Microvascular Techniques
Canadian institutionsSaint John Regional HospitalDalhousie University
Fundersnot available
KeywordsInnovatorMicrosurgeryMedicineSurgeryTransplantationReconstructive SurgeonGeneral surgeryPolitical scienceLaw

Abstract

fetched live from OpenAlex

Fu-Chan Wei is a world-renowned plastic and reconstructive surgeon. He is clearly one of the most influential and innovative surgeons in the history of plastic surgery. The Taiwanese legend is the innovator of the osteoseptocutaneous fibula flap, which revolutionized the reconstruction of composite bone and soft tissue defects in the jaw and extremities. He has pioneered several perforator flaps, including the free style variety. He has taken toe-to-hand microsurgical transplantation to a whole new level. He is not only recognized for his surgical skills and clinical innovations, but also for his vision, leadership, and teaching. The establishment and development of the famous Microsurgery Center at Chang Gung Memorial Hospital is unparalleled anywhere. The international fellowship program in microsurgery there remains the envy of all microsurgical trainees. Dr. Wei and his colleagues have trained and influenced more than 1,500 surgeons from all over the world. The aim of this video article is to share what we learned by interviewing Fu-Chan Wei at Chang Gung. The story of Fu Chan Wei, his colleagues, and the development of the Microsurgery Center in Taiwan is worth knowing.

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.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.176
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0000.000
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 head0.024
GPT teacher head0.265
Teacher spread0.241 · 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