Analysis of Liver-Directed Therapies in U.S. Cancer Patients
Bibliographic record
Abstract
BACKGROUND: The liver is a common site of primary and metastatic cancer. Liver-directed therapies are commonly used to treat cancer involving the liver. We report on the patterns, predictors, and outcomes of liver-directed therapies in hospitalized cancer patients in the United States. METHODS: Data were obtained from all U.S. states that contributed to the Nationwide Inpatient Sample maintained by the Agency for Healthcare Research and Quality between 2006 and 2010. Univariate and multivariate testing was used to identify factors significantly associated with patient outcome. RESULTS: For the 5-year period of interest, 12,540 patient discharges were identified. Mean age in the sample was 60 years. Primary liver lesions (n = 8840) made up 26.9% of the sample; the remaining cases were metastases. Most procedures were performed in large (79%) urban (98%) hospitals and in patients with insurance (97.9%). The most common intervention was partial hepatectomy (42.7%), followed by open (9.9%), percutaneous (7.2%), and laparoscopic (5.04%) ablation of liver lesions; embolization (9.8%); and liver transplantation (2.64%). The incidence of in-hospital mortality was very low (2.4%), and the complication rate was 12.2%. Complications such as acute liver necrosis, ascites, hepatic coma, hepatorenal syndrome, liver abscess, and high number of comorbid illnesses (>8) accounted for 60% of the in-hospital mortality. CONCLUSIONS: The low rate of morbidity and mortality associated with liver-directed therapies in hospitalized cancer patients supports the continuing utility of such procedures in the management of primary and metastatic liver cancer. The patterns of health disparities observed with respect to the use of liver-directed therapies are concerning.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".