Impact of <scp>Karnofsky</scp> performance status on outcomes of patients with severe alcohol‐associated hepatitis: a propensity‐matched analysis
Bibliographic record
Abstract
BACKGROUND AND AIMS: Severity scores, including the model for end-stage liver disease (MELD) and discriminant function score, guide the treatment of patients with severe alcohol-associated hepatitis (AH). We aimed to investigate the impact of functional status on outcomes of patients with AH. METHODS: Medically managed patients (n = 133) with AH from 1 January 2019 to 31 December 2022 were included in this prospective study. The objectives were to compare the long-term survival, recompensation rates, corticosteroid response, incidence of infections, hepatic encephalopathy (HE) and acute kidney injury (AKI) among propensity score-matched patients with good Karnofsky performance status (KPS) (score ≥50) and poor KPS (score <50) using Kaplan-Meier analysis. RESULTS: Twenty-five patients with good KPS were matched with 25 patients with poor KPS and followed up for a median duration of 10 (0.5-33) months. Survival was 76% (19/25; 95% confidence interval (CI), 54.9-90.6) in patients with good KPS compared to 42.3% (11/25; 95% CI, 23.4-63.1) patients with poor KPS (P = 0.001) at 10 months. The recompensation rate was higher in the good KPS group than in the poor KPS group (68% vs 44%; P = 0.04). A higher proportion of patients in the good KPS group (78.9%) than in the poor KPS group (42.8%; P = 0.03) responded to corticosteroids. Survival was lower among non-responders in the poor KPS group (0% vs 75%; P = 0.01). The proportion of patients who developed infection (36% vs 28%; P = 0.051), HE (36% vs 12%; P = 0.01) and AKI (60% vs 16%; P < 0.001) was higher in patients with poor KPS than in good KPS. CONCLUSIONS: KPS is an important determinant of outcomes in patients with AH, including survival, recompensation, response to corticosteroids and complications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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 teacher head, 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".