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Record W4307284961 · doi:10.1097/sap.0000000000003312

Youngest Composite Full-Face Transplant

2022· article· en· W4307284961 on OpenAlexaff
Rebecca Knackstedt, Maria Siemionow, Risal Djohan, Graham S. Schwarz, Bahar Bassiri Gharb, Antonio Rampazzo, Steven Bernard, Gaby Doumit, Raffi Gurunian, Bijan Eghtesad, Wilma F. Bergfeld, Debra Priebe, Frank Papay, Brian Gastman

Bibliographic record

VenueAnnals of Plastic Surgery · 2022
Typearticle
Languageen
FieldMedicine
TopicOrgan and Tissue Transplantation Research
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsMedicineImmunosuppressionCraniofacialPerioperativeTransplantationSurgeryInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: The field of face transplantation continues to evolve, with more complex defects being addressed, and, at the same time, increased outcome expectations. Given our unique long-term experience in this field, we consented one of the youngest patients to undergo a full-face transplant. METHODS: An 18-year-old woman presented with complete destruction of her central face and craniofacial structures. She had coexisting major injuries, including pituitary gland, visual axis, and motor control. After extensive rehabilitation and reconstruction techniques, the patient underwent face transplant on May 4, 2017, at the age of 21 years. RESULTS: The total operative time for the recipient was 26 hours. There were no major perioperative complications. Since transplant, the patient has undergone 3 revision surgeries. She is near completely independent from a daily life activity standpoint. She has had 1 episode of rejection above grade II that was successfully treated with a short-term increased in immunosuppression. CONCLUSIONS: Contrary to data in solid organ transplantation where youth is associated with increased risk of rejection, our current algorithm in immunosuppression, combined with this patient's compliance, has led to only 1 rejection episode beyond grade II. This successful transplant can serve as a model for future vascularized composite transplants in younger populations.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.122
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.092
GPT teacher head0.335
Teacher spread0.243 · 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 designObservational
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

Citations6
Published2022
Admission routes1
Has abstractyes

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