Management and Outcomes of Hospitalized Patients With Primary Neuroendocrine Tumor and Non-Neuroendocrine Tumor Appendiceal Cancers in the United States
Bibliographic record
Abstract
BACKGROUND: The incidence of appendiceal cancers continues to rise at a very rapid pace. Although surgery has a central role in the management of appendiceal tumors, literature is lacking regarding the pattern and predictors of surgical treatment for patients with appendiceal cancers. We aimed to describe the surgical treatment for patients with appendiceal cancers, with emphasis on utilization based on histology. METHODS: Hospitalized patients with appendiceal cancer in the US between 2006 and 2010 were included in the study. The Nationwide Inpatient Sample database maintained by the Agency for Health Care Research and Quality was employed for univariate and multivariate testing to identify factors significantly associated with patient outcome. RESULTS: A total of 3,799 patient discharges were identified over the 5-year period covered by the study. Neuroendocrine tumor (NET) was the diagnosis in 291 (7.66%) patients and non-NET in 3,508 (92.34%) patients. The mean age was 56.8 years (± SD 14.6), with female predominance (54.73% vs. 45.27%). NET patients were younger than those with non-NET (50.7 vs. 57.4 years; P < 0.001). NET patients were more commonly treated with appendectomy compared to non-NET (OR: 1.59; 95% CI: 1.23 - 2.07; P < 0.001). Hyperthermic intraperitoneal chemotherapy (HIPEC) was used in 8.5% of all the cases, mostly in non-NET histology (91% vs. 8%). Majority of the patients treated with HIPEC had no co-morbid medical illness (60%), and received care at high volume hospitals located in urban areas. There was a very low incidence of in-hospital mortality (2.5%). CONCLUSIONS: The described surgical utilization pattern should prompt more research focusing on barriers to appropriate surgical debulking and HIPEC utilization in non-NET appendiceal cancers.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".