Physiologic Reserve Assessment and Application in Clinical and Research Settings in Liver Transplantation
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".