S0246 Anal Cancer: Overview of Hospitalizations, Healthcare Utilization, and Outcomes from the Inpatient World
Bibliographic record
Abstract
INTRODUCTION: Anal cancer is an uncommon type of gastrointestinal malignancy that occurs in the anal canal and potentially cause signs and symptoms such as rectal bleeding and anal pain. Limited epidemiological data exist on cost of care and outcomes for hospitalizations with anal cancer. METHODS: National Inpatient Sample (NIS) database was analyzed during the period from 2001 to 2011 for all subjects with the diagnosis of anal cancer (ICD-9 code 154.3) as primary or secondary diagnosis. Nationwide Inpatient Sample (NIS) is the largest inpatient care database in the United States. Its large sample size is ideal for developing national and regional estimates. Cochran-Armitage trend test was used for determining statistical significance of variation. RESULTS: In 2001, there were 2,943 hospitalizations with anal cancer as compared to 5,587 in 2011 (P < 0.0001, Figure 1A). Age group 50–64 remained the most commonly affected with rising proportional trend from 32.5% in 2001 to 41.0% in 2011 (P < 0.0001, Figure 1B). Throughout the study period, more women (about two thirds each year) were hospitalized with anal cancer as compared to men (P < 0.0001, Figure 1C). Caucasians remained the most commonly affected race with a slight decrease in proportional trend from 78.4% in 2001 to 74.5% in 2011 (P < 0.0001). Southern US remained to have more anal cancer-related hospitalizations as compared to other regions, however, over the study period there was a decline in hospitalization rate from 44.7% in 2001 to 38.8% in 2011 (P < 0.0001). In- hospital mortality decreased from 3.2% in 2001 to 2.7% in 2011 (P < 0.0001) and average length of stay increased from 6.5 days in 2001 to 7.4 days in 2011 (P = 0.63). Cost per hospitalization increased from $10,749 in 2001 to $15,394 in 2011 (adjusted for inflation, P < 0.0001, Figure 1D). Analysis of the Agency for Healthcare Research and Quality (AHRQ) comorbidity measures revealed hypertension, fluid & electrolyte disorders, and deficiency anemias as some of the more commonly associated comorbidities with metastatic cancer showing increasing association over time (Figure 2).Figure 1Figure 2CONCLUSION: Significant rise in the number of hospitalizations with anal cancer was found with interesting demographic variations and association with comorbidities. Although in-hospital mortality decreased, there was a noteworthy rise in the cost of care. Further studies are needed to identify potential predictors & factors responsible for such results to better elucidate our findings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".