Clinical characteristics of human immunodeficiency virus patients being referred for liver transplant evaluation: a descriptive cohort study
Bibliographic record
Abstract
BACKGROUND: Liver transplantation (LT) is a treatment option for select human immunodeficiency virus (HIV)-infected patients with advanced liver disease. The aim of this study was to describe LT evaluation outcomes in HIV-infected patients. METHODS: All HIV-infected patients referred for their first LT evaluation at the Mount Sinai Medical Center were included in this retrospective, descriptive cohort study. Multivariable logistic regression was used to identify factors independently associated with listing. RESULTS: Between February 2000 and April 2012, 366 patients were evaluated for LT, with 66 (18.0%) listed for LT and 300 (82.0%) not listed. Fifty-one patients (13.9%) died before completing evaluation and 85 (23.2%) were too early for listing. Reasons patients were declined for listing were psychosocial (15.8%), HIV-related (10.4%), loss to follow-up (9.6%), surgical/medical (6.0%), liver-related (4.4%), patient choice (3.4%), and financial (1.6%). Listed patients were more likely to have hepatocellular carcinoma (HCC) (43.1% vs. 17.1%; P < 0.0001) and less likely to have hepatitis B (6.2% vs. 15.7%; P = 0.04) or a psychiatric history (19.7% vs. 35.2%; P = 0.02) than those not listed. In multivariable analysis, HCC (odds ratio [OR] 5.79; 95% confidence interval [95% CI]: 2.97-11.28), model for end-stage liver disease (MELD) score at referral (OR 1.06; 95% CI 1.01-1.11), and hepatitis B (OR 0.26; 95% CI 0.08-0.79) were associated with listing. CONCLUSION: MELD score and HCC were positive predictors of listing in HIV-infected patients referred for LT evaluation and, therefore, timely referrals are vital in these patients. As MELD is a predictor for death while undergoing evaluation, rapid evaluation should be performed in HIV-infected patients with a higher MELD score.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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 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".