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Record W3135105090 · doi:10.1002/lt.26052

Physiologic Reserve Assessment and Application in Clinical and Research Settings in Liver Transplantation

2021· review· en· W3135105090 on OpenAlexaff
Rahima A. Bhanji, Kymberly D. Watt

Bibliographic record

VenueLiver Transplantation · 2021
Typereview
Languageen
FieldMedicine
TopicLiver Disease and Transplantation
Canadian institutionsUniversity of Alberta HospitalAlberta Hospital Edmonton
Fundersnot available
KeywordsMedicineLiver transplantationIntensive care medicineTransplantationInternal medicine

Abstract

fetched live from OpenAlex

Physiologic reserve is an important prognostic indicator. Because of its complexity, no single test can measure an individual's physiologic reserve. Frailty is the phenotypic expression of decreased reserve and portends poor prognosis. Both subjective and objective tools have been used to measure one or more components of physiologic reserve. Most of these tools appear to predict pretransplant mortality, but only some predict posttransplant survival. Incorporation of these measures of physiologic reserve in the clinical and research settings including prediction models are reviewed, and the applicability to patient-related outcomes are discussed. Commonly used tools, in patients with cirrhosis, that have been associated with clinical outcomes were reviewed. The strength of subjective tools lies in low-cost, wide availability, and quick assessments at the bedside. A disadvantage of these tools is the manipulative capacity, restricting their value in allocation processes. The strength of objective tests lies in objective measurements and the ability to measure change. The disadvantages include complexity, increased cost, and limited accessibility. Heterogeneity in the definitions and tools used has prevented further advancement or a clear role in transplant assessment. Consistent use of objective tools, including the 6-minute walk test, gait speed, Liver Frailty Index, or Short Physical Performance Battery, are recommended in clinical and research settings.

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)0.002
metaresearch head score (Gemma)0.000
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.856
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.119
GPT teacher head0.465
Teacher spread0.346 · 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
GenreReview

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

Citations19
Published2021
Admission routes1
Has abstractyes

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