Histone Lactylation-Driven Ubiquitin-Specific Protease 34 Promotes Cisplatin Resistance in Hepatocellular Carcinoma
Bibliographic record
Abstract
Background: Gastrointestinal stromal tumors (GISTs) are associated with a substantial economic burden to the healthcare system despite their relatively low incidence and prevalence compared to other more common malignancies. This study aimed to evaluate trends in GIST-related hospitalizations, inpatient mortality, and the financial burden of GISTs in the United States. Methods: The National Inpatient Sample (NIS) database from 2016 to 2020 was used to identify adult hospitalizations (age > 18 years) with a primary diagnosis of GIST. A multivariate logistic regression along with Chi-square and t-tests was performed using SAS 9.4 software to analyze inpatient GIST-associated mortality, inflation-adjusted total hospital charge (THC), and length of stay (LOS) during the study period. Results: The study analyzed 48,690 hospitalizations (49.2% female, mean age 64.2 years, 38.2% elective admissions) with a primary diagnosis of GIST between 2016 and 2020. Annual GIST-related hospitalizations increased from 2,645 in 2016 to 11,565 in 2020 (P = 0.1208). The most common tumor location was stomach (48.5%), followed by small intestine (18.7%), large intestine (3.6%), and rectum (1.6%). There was a non-significant reduction in inpatient mortality from 4.16% in 2016 to 3.29% in 2020 (P = 0.807). Overall, 36.2% of patients had THC between $10,000 and $20,000 (36.5% in 2016 vs. 34.7% in 2020, P = 0.0001), and 9.8% of patients had a THC > $40,000 (8.3% in 2016 vs. 12.6% in 2020, P = 0.0001). Furthermore, 61.5% of patients had LOS of fewer than 5 days (59.16% in 2016 vs. 61.39% by 2020, P = 0.0001), and 38.5% had LOS of 5 days or more (40.84% in 2016 vs. 38.61% in 2020, P = 0.0001). The proportion of GISTs treated with endoscopic resection has remained stable with 13.02% in 2016 and 13.01% in 2020 (P = 0.08). Additionally, the proportion of surgical excisions decreased from 26.8% in 2016 to 21.4% in 2020 with a statistically significant trend (P = 0.004). Conclusions: GIST-related inpatient mortality between 2016 and 2020 has remained stable, and endoscopic and surgical interventions have become more common for the management of GISTs. This has been accompanied by a significant rise in overall inflation-adjusted hospitalization costs in the study period. These findings highlight the need for continued optimization of care and resource allocation for GIST management.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".