The Relationship Between Number of Comorbidities and Age of Colorectal Cancer Diagnosis in US Male Veteran Population: A Single-Center Experience
Bibliographic record
Abstract
BACKGROUND: Comorbidities of tobacco and alcohol abuse and obesity are major risk factors for colon carcinogenesis. These risk factors are considered the most prevalent modifiable risk factors linked to malignancies including colorectal cancer (CRC) in both high- and low-income countries. The aim of this study was to investigate the relationship between number of comorbidities and age of CRC diagnosis in US male veteran population. METHODS: A retrospective single-center study using chart review and the International Classification of Diseases, Ninth Revision (ICD-9) codes to identify patients with a diagnosis of CRC and comorbidities of tobacco abuse, alcohol abuse, hypertension (HTN), diabetes mellitus (DM) and chronic kidney disease (CKD). The primary aim was to study effect of these comorbidities on age of CRC diagnosis. Univariable and then multivariable logistic regression models were fit to age at diagnosis for each patient variable. RESULTS: A total of 362 patients were included in the study. The mean age of CRC diagnosis was 66.8. Eighty percent were Caucasians, and 20% were African Americans. African Americans were diagnosed with CRC 3.8 years younger compared to Caucasians (P = 0.01). Controlling for other variables in the multivariable model, age at CRC diagnosis was significantly lower for African Americans and for patients with higher total counts for tobacco and alcohol abuse and obesity. HTN, DM and CKD were not associated with a lower age of CRC diagnosis. CONCLUSIONS: Tobacco and alcohol abuse and obesity have negative cumulative effect on age of CRC diagnosis in US male veteran population. Patients with increasing number of these comorbidities are associated with significantly lower age of CRC diagnosis. It is important to identify veterans with these comorbidities and encourage CRC screening.
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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".