Palliative care access for hospitalized patients with end‐stage liver disease across the United States
Bibliographic record
Abstract
Patients with end-stage liver disease (ESLD) often have a high symptom burden. Historically, palliative care (PC) services have been underused in this population. We investigated the use of PC services in patients with ESLD hospitalized across the United States. We used the Nationwide Inpatient Sample to conduct a retrospective nationwide cohort analysis. All patients >18 years of age admitted with ESLD, defined as those with at least two liver decompensation events, were included in the analysis. A multivariate logistic regression model predicting referral to PC was created. We analyzed 55,208,382 hospitalizations from the 2006-2012 Nationwide Inpatient Sample, with 39,349 (0.07%) patients meeting study inclusion. PC consultation was performed in 1,789 (4.5%) ESLD patients. The rate of PC referral in ESLD increased from 0.97% in 2006 to 7.1% in 2012 (P < 0.01). In multivariate analysis, factors associated with lower referral to PC were Hispanic race (odds ratio [OR], 0.77; 95% confidence interval [CI], 0.66-0.89; P < 0.01) and insurance coverage (OR, 0.74; 95% CI, 0.65-0.84; P < 0.01). Factors associated with increased referral to PC were age (per 5-year increase, OR, 1.05; 95% CI, 1.03-1.08; P < 0.01), do-not-resuscitate status (OR, 16.24; 95% CI, 14.20-18.56; P < 0.01), treatment in a teaching hospital (OR, 1.25; 95% CI, 1.12-1.39; P < 0.01), presence of hepatocellular carcinoma (OR, 2.00; 95% CI, 1.71-2.33; P < 0.01), and presence of metastatic cancer (OR, 2.39; 95% CI, 1.80-3.18; P < 0.01). PC referral was most common in west coast hospitals (OR, 1.81; 95% CI, 1.53-2.14; P < 0.01) as well as large-sized hospitals (OR, 1.49; 95% CI, 1.22-1.82; P < 0.01). CONCLUSION: From 2006 to 2012 the use of PC in ESLD patients increased substantially; socioeconomic, geographical, and ethnic barriers to accessing PC were observed. (Hepatology 2017;66:1585-1591).
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| 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 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".