S579 Race-Specific Mortality from Esophageal Cancer in the United States
Bibliographic record
Abstract
Introduction: In the United States, esophageal cancer represents approximately 1% of the total cancer diagnoses. In the United States, esophageal adenocarcinoma is the more frequent subtype compared to esophageal squamous cell carcinoma, comprising approximately 80% of all cases of esophageal cancer. The objective of this study was to analyze the patterns of mortality from esophageal cancer among different racial groups in the United States Methods: We conducted a retrospective analysis employing the Centers for Disease Control and Prevention's (CDC) WONDER database, which covers the entire US population. Using the multiple cause of death database (International Classification of Disease - 10th revision), we identified all patients who died of Esophageal Cancer (C15.0.x listed as the underlying cause of death) between 1999 and 2020 in the United States. Then we stratified data by race regardless of gender, and ethnic background. Age-adjusted mortality rates were calculated per 1000,000 persons (PMP) and standardized to the US census data from 2000. Results: A total of 309,919 Esophageal Cancer deaths with an overall age-adjusted mortality rate of 41.3 PMP were identified between 1999 and 2020. Race specific age-adjusted mortality were 42.6 PMP, 40.8 PMP, 25.4 PMP, and 16.6 PMP in White, Black, Asian or Pacific Islander, and American Indian or Alaska native populations, respectively. The age-adjusted mortality decreased by 5% in White (from 41.9 PMP in 1999 to 39.7 PMP in 2020), decreased by 60% in Black (from 65.3 PMP in 1999 to 26.2 PMP in 2019), decreased by 31 % in Asian/Pacific Islander (from 21.3 PMP in 1999 to 14.7 PMP in 2020), and decreased by 13% in American Indian/Alaska Native (25.9 in 1999 to 20.3 in 2020). Conclusion: Between 1999 and 2020, age adjusted mortality decreased in all races. White had the highest overall age adjusted mortality, followed by Black, Asian/Pacific Islander, and American Indian/Alaska native in descending order. Interestingly, Black showed the highest rate of decline in mortality while White showed the lowest.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| 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) | 0.005 | 0.001 |
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".