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Record W2613219028 · doi:10.1055/s-2006-955121

Decision Analysis Model for Facial Composite Tissue Allotransplantation

2006· article· en· W2613219028 on OpenAlexaff
Sabrina Cugno, Sheila Sprague, Eric Duku

Bibliographic record

VenueJournal of Reconstructive Microsurgery · 2006
Typearticle
Languageen
FieldMedicine
TopicOrgan and Tissue Transplantation Research
Canadian institutionsMcMaster University
Fundersnot available
KeywordsAllotransplantationMedicineDisfigurementQuality of life (healthcare)SurgeryQuality-adjusted life yearDecision analysisTransplantationRisk analysis (engineering)StatisticsCost effectiveness

Abstract

fetched live from OpenAlex

Facial composite tissue allotransplantation (CTA) has been proposed as a potential reconstructive option in severe facial disfigurement, in view of the reported success of hand allotransplantation. In the absence of clinical data, the decision to proceed with facial allotransplantation is dependent on the value or expected utility of the resultant status. Utility is measured by various means, including quality adjusted life years (QALYs). The QALY was developed as an attempt to integrate length of life in a particular health state and quality of life in that state into a single index measure. The change in utility value effected by an intervention multiplied by the duration of the treatment effect provides the number of QALYs gained. Utilities expressed as QALYs can then be fitted into a decision analytic model. Decision analysis enables surgeons to compare the expected consequences of pursuing different strategies (e.g., facial CTA vs. severe facial disfigurement). The purpose of this study was to assist surgeons with the decision of whether to proceed with CTA of the face. The principal complications associated with facial allotransplantation were identified by a comprehensive review of kidney transplant and hand allotransplant literature. Based on the latter, the probabilities associated with the occurrence of each complication were derived, and fitted into a decision analytic “tree.” The decision analytic tree was constructed illustrating possible health states (pathways) for facial allotransplantation. The QALYs gained with transplantation were obtained from a sample of convenience (n = 60) that included various health care professionals. Utilities were computed from values obtained with the “feeling thermometer” (FG), standard gamble (SG), and time trade-off (TTO) measures. Quality adjusted life years for severe facial deformity was 11.2, 16.0, and 17.5 with the FT, SG, and TTO measures, respectively. Following facial allotransplantation, QALYs were 37.4, 32.6, and 31.1 with the above listed measures. The current debate within the medical community surrounding facial CTA has centered on the issue of inducing a state of immunocompromise in a physically healthy individual for a non-life-saving procedure. However, the latter must be weighed against the potential social and psychological benefit transplantation would confer. As demonstrated by a mean gain of 33.7 QALYs, participants' valuation of quality of life is notably greater for facial transplantation and the side effects of immunosuppression than for a state of uncompromised physical health with severe facial disfigurement.

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)0.006
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.026
Threshold uncertainty score0.061

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.010
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0040.001
Open science0.0030.002
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0180.002

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 head0.020
GPT teacher head0.317
Teacher spread0.297 · 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 designSimulation or modeling
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

Citations0
Published2006
Admission routes1
Has abstractyes

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