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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 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

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.238
Threshold uncertainty score0.439

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.020
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