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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".